Estimating the causal effect of interventions in a state-level
Kenneth Lee
10/11/2020
Introduction
In this notebook, our goal is to estimate the causal effect of different state policies on mobility signal in a state-level.
We will look at a number of states that have enforced the same type of policies, both mandatory and non-mandatory, and see if the effect of the same intervention may vary from state to state. The mobility signals are from Delphi Epidata API, which include full_time_work_prop, part_time_work_prop, completely_home_prop, and median_home_dwell_time.
The policy data is from University of Washington’s State-level social distancing policies as we will use it in model building. For the definition of the policy, please refer to the codebooks.
Based on exploratory data analysis, there is a significant decrase in mobility signal during weekends, we will drop all the weekends in the data throughout this analysis.
library(ggplot2)
library (readr)
library(tidyverse)
library(dplyr)
library(covidcast)
library(lubridate)
library(ggpubr)
library(reshape2)
library(tidyr)
library(viridis)
library(gridExtra)
library(zoo)
library(cowplot)
library(gplots)
library(car)
library(nortest)
library(mgcv)
#library(MASS)
source("code/painter.r")
source("code/loader.r")
source("code/parser.r")STARTDATE <- "2019-01-01"
ENDDATE <- lubridate::today()
GEO_TYPE = "state" # state-level
GEO_VALUE = "*" # all states
EXCLUDED_AREAS = c("as","gu", "mp","vi", "pr") # excluded areas due to small sample size# Full time away home mobility
ftime <- covidcast_signal(data_source = "safegraph",
signal ="full_time_work_prop",
start_day = STARTDATE,
end_day = ENDDATE,
geo_type = GEO_TYPE,
geo_values = GEO_VALUE)
# filter out a few states
ftime <- ftime %>%
filter(!(geo_value %in% EXCLUDED_AREAS))
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
chome <- covidcast_signal(data_source = "safegraph",
signal ="completely_home_prop",
start_day = STARTDATE,
end_day = ENDDATE,
geo_type = GEO_TYPE,
geo_values = GEO_VALUE)
# filter out a few states
chome <- chome %>%
filter(!(geo_value %in% EXCLUDED_AREAS))
# The median time spent at home for all devices at this location for this time period, in minutes
mhome<- covidcast_signal(data_source = "safegraph",
signal ="median_home_dwell_time",
start_day = STARTDATE,
end_day = ENDDATE,
geo_type = GEO_TYPE,
geo_values = GEO_VALUE)
# filter out a few states
mhome <- mhome %>%
filter(!(geo_value %in% EXCLUDED_AREAS))
# Read government intervention data
policy <- load_policy()Analysis
We would like to look at policies that have been implemented by all the states: school closure, restaurant restriction, emergency declaration, and bar restriction. The full list of policies are listed below (please refer to the codebooks for detailed definitions):
EmergDec: Emergency declaration; currently includes State of Emergency, Public Health Emergency, Public Health Disaster declarations, Civil Emergency declarations, and other permutations of state‐level declarations of emergency in response to COVID‐19.SchoolClose: Formal closing of (at minimum) public K‐12 schools.BarRestrict: Restriction or limitation of bars, breweries,wineries, tasting rooms, and/or other venues where alcoholic beverages are consumed on‐premises and sales of on‐site alcohol consumption is the primary function of the venue (i.e., bars/bar areas contained within restaurants are coded within RestaurantRestrict, as are venues that may be called bars, pubs, etc. but have food licenses)GathRestrict: gathering restrictionOtherBusinessClose: Mandate to close or substantially reduce operations of any category of business that are not classified under restaurants or bars.RestaurantRestrict: Restriction or limitation of restaurants and other venues where food is consumed on‐premises. Establishments where alcohol is served and may be called a bar or like venue but have a food license are coded within the RestaurantRestrict policy category as they are viewed as operating more like a restaurant than a bar.CaseIsolation: Policy that requires individuals with confirmed coronavirus infection (via testing) or suspected infection to self‐isolate for a specified period of time, or when they no longer test positive for infection.StayAtHome: Policy instructing individuals to stay at home for all non‐essential activities. Coding a case as a stay‐at‐home order mandate requires the executive order to using phrasing indicative of a mandate (e.g., “must stay at home”); otherwise it is coded as 0 for the “Mandate” variable if it uses advisory phrasing.PublicMask: Policy that recommends or requires individuals to wear masks or other mouth and nose coverings when they are outside their places of residence in the public.Quarantine: Quarantines mandated for people entering the state, requiring a period of self‐isolation. Quarantines may be imposed on all people entering the state, out‐of‐ state residents, or travelers from a particular state or city.NEBusinessClose: Mandate to close all non‐essential businesses. Coding a case as a closure order requires the executive order to use phrasing indicative of a mandate (e.g., “non‐essential businesses are required to close”, “non‐essential businesses must cease operations by date”).TravelRestrictIntra: Restrictions on travel within the state.TravelRestrictEntry: Travel restriction mandates that limit non‐residents from entering a given state.SchoolMask: Policy that involves requiring students to wear masks or other mouth and nose coverings while at school.TravelRestrictExit: Policies which prohibit residents of a state from leaving the state.BusinessMask: Policy that involves requiring employees to wear masks or other mouth and nose coverings as part of business operations.
Then, we focus on a number of states, in which some of them enforce one of the policies as mandatory, and some of them implement the policy as a recommendation.
# We filter down to only state wide policy
policy <- policy %>%
filter(StateWide == 1)Regression Discontinuity Design
First, we look at the simplest regression discontinuty (RD) design by regressing mobility on time in different states.
School Closure
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='SchoolClose' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact school closure policy as a mandate:") ## [1] "States that enact school closure policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "Colorado" "Connecticut"
## [7] "Delaware" "District of Columbia" "Florida"
## [10] "Georgia" "Hawaii" "Idaho"
## [13] "Illinois" "Indiana" "Iowa"
## [16] "Kansas" "Louisiana" "Maine"
## [19] "Maryland" "Massachusetts" "Michigan"
## [22] "Minnesota" "Mississippi" "Missouri"
## [25] "Montana" "Nebraska" "Nevada"
## [28] "New Hampshire" "New Jersey" "New Mexico"
## [31] "New York" "North Carolina" "North Dakota"
## [34] "Ohio" "Oklahoma" "Oregon"
## [37] "Pennsylvania" "Rhode Island" "South Carolina"
## [40] "Texas" "Utah" "Vermont"
## [43] "Virginia" "Washington" "West Virginia"
## [46] "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='SchoolClose' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact school closure policy as a recommendation:") ## [1] "States that enact school closure policy as a recommendation:"print(nonmandate.states$StateName)## [1] "California" "Iowa" "Kentucky" "South Dakota" "Tennessee"# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of school closure")## [1] "States that have changed the mandatory status of school closure"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## [1] "Iowa"California
plotRD(ftime,
policy,
"SchoolClose",
"ca",
STARTDATE,
ENDDATE)
Tennessee
plotRD(ftime,
policy,
"SchoolClose",
"tn",
STARTDATE,
ENDDATE)
New York
plotRD(ftime,
policy,
"SchoolClose",
"ny",
STARTDATE,
ENDDATE)
Alabama
plotRD(ftime,
policy,
"SchoolClose",
"al",
STARTDATE,
ENDDATE)
All-in-one
states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"SchoolClose",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Emergency Declaration
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='EmergDec' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact emergency declaration policy as a mandate:") ## [1] "States that enact emergency declaration policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='EmergDec' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact emergency declaration policy as a recommendation:") ## [1] "States that enact emergency declaration policy as a recommendation:"print(nonmandate.states$StateName)## character(0)# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of emergency declaration")## [1] "States that have changed the mandatory status of emergency declaration"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## character(0)All-in-one
states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"EmergDec",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Bar Restriction
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='BarRestrict' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact bar restriction policy as a mandate:") ## [1] "States that enact bar restriction policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='BarRestrict' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact bar restriction policy as a recommendation:") ## [1] "States that enact bar restriction policy as a recommendation:"print(nonmandate.states$StateName)## [1] "California"# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of bar restriction")## [1] "States that have changed the mandatory status of bar restriction"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## [1] "California"states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"BarRestrict",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Restaurant Restriction
# Look at where has enacted school close as a mandate
mandate.states <- policy %>%
filter(StatePolicy=='RestaurantRestrict' & Mandate==1) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique %>%
as.list
print("States that enact restaurant restriction policy as a mandate:") ## [1] "States that enact restaurant restriction policy as a mandate:"print(mandate.states$StateName)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"# Look at where school close is enacted as a recommendation
nonmandate.states <- policy %>%
filter(StatePolicy=='RestaurantRestrict' & Mandate==0) %>%
dplyr::select(StateName) %>%
arrange(StateName) %>%
unique() %>%
as.list %>%
as.vector
print("States that enact restaurant restriction policy as a recommendation:") ## [1] "States that enact restaurant restriction policy as a recommendation:"print(nonmandate.states$StateName)## [1] "California"# Check if any state has changed their school close from mandate to recommendation, or the other way around
print("States that have changed the mandatory status of restaurant restriction")## [1] "States that have changed the mandatory status of restaurant restriction"dplyr::intersect(nonmandate.states$StateName,
mandate.states$StateName)## [1] "California"states <- unique(ftime$geo_value)
plist <- list()
count <- 1
for(state in states){
p <- plotRD(ftime,
policy,
"RestaurantRestrict",
state,
STARTDATE,
ENDDATE,
plotMultiple = T)
plist[[count]] <- p
count = count + 1
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=nCol))
Linear Regression
\[y = \beta_{0} + \beta_{1}t + \beta_{2}I_t + \beta_{3}tI_t\]
Full-time-work-prop
Emergency Declaration
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*EmergDec,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.030378 -0.006689 -0.001319 0.006600 0.042003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.820e-01 8.846e-02 -5.449 8.10e-08 ***
## time_value 2.967e-05 4.883e-06 6.076 2.51e-09 ***
## EmergDec -5.914e-01 2.373e-01 -2.493 0.0130 *
## time_value:EmergDec 3.139e-05 1.289e-05 2.435 0.0153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01088 on 480 degrees of freedom
## Multiple R-squared: 0.1229, Adjusted R-squared: 0.1174
## F-statistic: 22.41 on 3 and 480 DF, p-value: 1.35e-13
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057077 -0.008910 0.001088 0.010290 0.042576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.260e+00 1.317e-01 -9.568 < 2e-16 ***
## time_value 7.357e-05 7.268e-06 10.122 < 2e-16 ***
## EmergDec -1.956e+00 3.616e-01 -5.411 9.93e-08 ***
## time_value:EmergDec 1.037e-04 1.964e-05 5.280 1.96e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01634 on 480 degrees of freedom
## Multiple R-squared: 0.4014, Adjusted R-squared: 0.3977
## F-statistic: 107.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054876 -0.008615 0.002204 0.009172 0.037126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.093e+00 1.209e-01 -9.042 < 2e-16 ***
## time_value 6.414e-05 6.672e-06 9.613 < 2e-16 ***
## EmergDec -1.476e+00 3.242e-01 -4.553 6.72e-06 ***
## time_value:EmergDec 7.799e-05 1.761e-05 4.428 1.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01486 on 480 degrees of freedom
## Multiple R-squared: 0.3704, Adjusted R-squared: 0.3664
## F-statistic: 94.12 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043346 -0.005020 0.001015 0.005986 0.031184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.790e-01 8.148e-02 -8.334 8.28e-16 ***
## time_value 4.080e-05 4.498e-06 9.071 < 2e-16 ***
## EmergDec -4.257e-01 2.185e-01 -1.948 0.0520 .
## time_value:EmergDec 2.149e-05 1.187e-05 1.810 0.0709 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01002 on 480 degrees of freedom
## Multiple R-squared: 0.4358, Adjusted R-squared: 0.4323
## F-statistic: 123.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044309 -0.004451 0.000541 0.005761 0.033678
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.479e-01 8.353e-02 -4.164 3.70e-05 ***
## time_value 2.255e-05 4.612e-06 4.889 1.39e-06 ***
## EmergDec -9.467e-02 2.116e-01 -0.447 0.655
## time_value:EmergDec 4.111e-06 1.150e-05 0.357 0.721
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01003 on 480 degrees of freedom
## Multiple R-squared: 0.2615, Adjusted R-squared: 0.2569
## F-statistic: 56.65 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051150 -0.005522 0.001215 0.008097 0.044722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.870e-01 1.009e-01 -7.799 3.93e-14 ***
## time_value 4.731e-05 5.571e-06 8.492 2.56e-16 ***
## EmergDec -6.156e-01 2.707e-01 -2.274 0.0234 *
## time_value:EmergDec 3.145e-05 1.471e-05 2.139 0.0330 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01241 on 480 degrees of freedom
## Multiple R-squared: 0.4364, Adjusted R-squared: 0.4329
## F-statistic: 123.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064344 -0.006756 0.001151 0.011844 0.069762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.064e+00 1.429e-01 -7.450 4.38e-13 ***
## time_value 6.312e-05 7.888e-06 8.002 9.29e-15 ***
## EmergDec -4.390e-01 3.788e-01 -1.159 0.247
## time_value:EmergDec 2.095e-05 2.059e-05 1.018 0.309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01749 on 480 degrees of freedom
## Multiple R-squared: 0.4738, Adjusted R-squared: 0.4705
## F-statistic: 144.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045601 -0.004948 -0.000484 0.006375 0.045223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.020e-01 9.437e-02 -9.558 <2e-16 ***
## time_value 5.325e-05 5.210e-06 10.221 <2e-16 ***
## EmergDec 2.580e-01 2.531e-01 1.019 0.309
## time_value:EmergDec -1.593e-05 1.375e-05 -1.158 0.247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0116 on 480 degrees of freedom
## Multiple R-squared: 0.4397, Adjusted R-squared: 0.4362
## F-statistic: 125.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054892 -0.005178 0.000583 0.008168 0.026662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.021e+00 1.051e-01 -9.710 <2e-16 ***
## time_value 6.016e-05 5.803e-06 10.367 <2e-16 ***
## EmergDec -1.820e-01 2.887e-01 -0.630 0.529
## time_value:EmergDec 7.444e-06 1.568e-05 0.475 0.635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01305 on 480 degrees of freedom
## Multiple R-squared: 0.5142, Adjusted R-squared: 0.5112
## F-statistic: 169.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050349 -0.004386 0.000390 0.005833 0.036668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.679e-01 8.585e-02 -10.110 < 2e-16 ***
## time_value 5.128e-05 4.740e-06 10.820 < 2e-16 ***
## EmergDec -7.653e-01 2.250e-01 -3.401 0.000727 ***
## time_value:EmergDec 3.985e-05 1.223e-05 3.259 0.001199 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01046 on 480 degrees of freedom
## Multiple R-squared: 0.4187, Adjusted R-squared: 0.4151
## F-statistic: 115.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057208 -0.005302 0.001941 0.008149 0.024139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.524e-01 1.034e-01 -8.245 1.59e-15 ***
## time_value 5.065e-05 5.707e-06 8.876 < 2e-16 ***
## EmergDec -1.672e+00 2.839e-01 -5.890 7.28e-09 ***
## time_value:EmergDec 8.877e-05 1.542e-05 5.756 1.54e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01283 on 480 degrees of freedom
## Multiple R-squared: 0.4312, Adjusted R-squared: 0.4277
## F-statistic: 121.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0153933 -0.0026392 0.0000795 0.0022947 0.0134726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.301e-01 3.338e-02 -9.890 <2e-16 ***
## time_value 1.988e-05 1.843e-06 10.787 <2e-16 ***
## EmergDec -2.374e-02 8.455e-02 -0.281 0.779
## time_value:EmergDec 9.436e-07 4.596e-06 0.205 0.837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.004007 on 480 degrees of freedom
## Multiple R-squared: 0.2283, Adjusted R-squared: 0.2235
## F-statistic: 47.33 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076301 -0.011374 0.001685 0.014651 0.076862
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.836e+00 1.781e-01 -10.308 < 2e-16 ***
## time_value 1.061e-04 9.833e-06 10.789 < 2e-16 ***
## EmergDec -1.448e+00 4.668e-01 -3.101 0.00204 **
## time_value:EmergDec 7.499e-05 2.537e-05 2.956 0.00327 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02169 on 480 degrees of freedom
## Multiple R-squared: 0.4301, Adjusted R-squared: 0.4265
## F-statistic: 120.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051391 -0.005566 0.000795 0.007388 0.027326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.694e-01 8.951e-02 -8.597 < 2e-16 ***
## time_value 4.599e-05 4.941e-06 9.308 < 2e-16 ***
## EmergDec -8.960e-01 2.458e-01 -3.645 0.000296 ***
## time_value:EmergDec 4.714e-05 1.335e-05 3.531 0.000454 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01111 on 480 degrees of freedom
## Multiple R-squared: 0.3329, Adjusted R-squared: 0.3287
## F-statistic: 79.85 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069228 -0.007947 0.000149 0.010178 0.068106
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.558e+00 1.522e-01 -10.238 <2e-16 ***
## time_value 9.040e-05 8.404e-06 10.757 <2e-16 ***
## EmergDec 4.223e-01 3.990e-01 1.059 0.290
## time_value:EmergDec -2.633e-05 2.168e-05 -1.214 0.225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01854 on 480 degrees of freedom
## Multiple R-squared: 0.4976, Adjusted R-squared: 0.4945
## F-statistic: 158.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053767 -0.006984 0.000354 0.009216 0.049958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.063e+00 1.114e-01 -9.542 < 2e-16 ***
## time_value 6.233e-05 6.153e-06 10.130 < 2e-16 ***
## EmergDec -7.761e-01 2.888e-01 -2.687 0.00745 **
## time_value:EmergDec 4.007e-05 1.570e-05 2.553 0.01098 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01351 on 480 degrees of freedom
## Multiple R-squared: 0.3862, Adjusted R-squared: 0.3824
## F-statistic: 100.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061325 -0.009620 0.001803 0.010907 0.034119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.398e+00 1.386e-01 -10.084 < 2e-16 ***
## time_value 8.134e-05 7.654e-06 10.628 < 2e-16 ***
## EmergDec -1.661e+00 3.763e-01 -4.414 1.25e-05 ***
## time_value:EmergDec 8.724e-05 2.044e-05 4.268 2.38e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01713 on 480 degrees of freedom
## Multiple R-squared: 0.4553, Adjusted R-squared: 0.4518
## F-statistic: 133.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058530 -0.007339 0.000977 0.010667 0.055775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.286e+00 1.324e-01 -9.716 <2e-16 ***
## time_value 7.493e-05 7.309e-06 10.252 <2e-16 ***
## EmergDec 4.400e-02 3.430e-01 0.128 0.898
## time_value:EmergDec -4.921e-06 1.864e-05 -0.264 0.792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01604 on 480 degrees of freedom
## Multiple R-squared: 0.4006, Adjusted R-squared: 0.3969
## F-statistic: 106.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048332 -0.007680 0.000608 0.008524 0.041921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.164e+00 1.102e-01 -10.557 < 2e-16 ***
## time_value 6.804e-05 6.086e-06 11.181 < 2e-16 ***
## EmergDec -1.232e+00 2.957e-01 -4.168 3.65e-05 ***
## time_value:EmergDec 6.477e-05 1.607e-05 4.032 6.44e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01355 on 480 degrees of freedom
## Multiple R-squared: 0.4045, Adjusted R-squared: 0.4008
## F-statistic: 108.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057219 -0.006416 0.000903 0.010845 0.058011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.485e-01 1.331e-01 -7.127 3.79e-12 ***
## time_value 5.657e-05 7.347e-06 7.700 7.83e-14 ***
## EmergDec -1.745e-01 3.529e-01 -0.495 0.621
## time_value:EmergDec 6.844e-06 1.917e-05 0.357 0.721
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01629 on 480 degrees of freedom
## Multiple R-squared: 0.4705, Adjusted R-squared: 0.4672
## F-statistic: 142.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059349 -0.007046 0.000148 0.009140 0.054341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.065e+00 1.259e-01 -8.463 3.18e-16 ***
## time_value 6.286e-05 6.950e-06 9.045 < 2e-16 ***
## EmergDec 6.671e-01 3.225e-01 2.069 0.0391 *
## time_value:EmergDec -3.887e-05 1.753e-05 -2.217 0.0271 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01518 on 480 degrees of freedom
## Multiple R-squared: 0.5019, Adjusted R-squared: 0.4988
## F-statistic: 161.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046076 -0.006947 0.001447 0.009004 0.035059
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.556e-01 1.106e-01 -7.738 5.99e-14 ***
## time_value 5.103e-05 6.103e-06 8.361 6.77e-16 ***
## EmergDec -5.564e-01 3.036e-01 -1.832 0.0675 .
## time_value:EmergDec 2.825e-05 1.649e-05 1.712 0.0875 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01372 on 480 degrees of freedom
## Multiple R-squared: 0.3706, Adjusted R-squared: 0.3667
## F-statistic: 94.22 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046585 -0.009257 -0.000371 0.009408 0.056409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.884e-01 8.955e-02 -4.338 1.76e-05 ***
## time_value 2.510e-05 4.935e-06 5.087 5.24e-07 ***
## EmergDec -8.448e-01 3.901e-01 -2.166 0.0308 *
## time_value:EmergDec 4.428e-05 2.118e-05 2.091 0.0371 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01514 on 480 degrees of freedom
## Multiple R-squared: 0.3341, Adjusted R-squared: 0.3299
## F-statistic: 80.28 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067352 -0.007986 0.000345 0.011043 0.037877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.559e+00 1.495e-01 -10.425 <2e-16 ***
## time_value 9.047e-05 8.253e-06 10.962 <2e-16 ***
## EmergDec -1.875e-03 4.106e-01 -0.005 0.996
## time_value:EmergDec -3.432e-06 2.230e-05 -0.154 0.878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01856 on 480 degrees of freedom
## Multiple R-squared: 0.5201, Adjusted R-squared: 0.5171
## F-statistic: 173.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066075 -0.008910 0.000945 0.010679 0.037603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.421e+00 1.396e-01 -10.177 < 2e-16 ***
## time_value 8.280e-05 7.708e-06 10.743 < 2e-16 ***
## EmergDec -1.601e+00 3.835e-01 -4.174 3.56e-05 ***
## time_value:EmergDec 8.386e-05 2.083e-05 4.026 6.59e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01733 on 480 degrees of freedom
## Multiple R-squared: 0.4636, Adjusted R-squared: 0.4602
## F-statistic: 138.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051156 -0.007830 0.000957 0.009864 0.028997
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.675e-01 1.162e-01 -8.325 8.83e-16 ***
## time_value 5.718e-05 6.415e-06 8.914 < 2e-16 ***
## EmergDec -2.055e+00 3.191e-01 -6.438 2.94e-10 ***
## time_value:EmergDec 1.096e-04 1.734e-05 6.322 5.91e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01442 on 480 degrees of freedom
## Multiple R-squared: 0.362, Adjusted R-squared: 0.358
## F-statistic: 90.77 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047665 -0.006456 0.000699 0.007393 0.027435
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.994e-01 9.096e-02 -8.788 <2e-16 ***
## time_value 4.787e-05 5.021e-06 9.534 <2e-16 ***
## EmergDec -5.746e-01 2.469e-01 -2.328 0.0203 *
## time_value:EmergDec 2.974e-05 1.341e-05 2.218 0.0270 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01124 on 480 degrees of freedom
## Multiple R-squared: 0.304, Adjusted R-squared: 0.2997
## F-statistic: 69.89 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050952 -0.005170 0.001172 0.008029 0.048028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.744e-01 9.848e-02 -7.864 2.48e-14 ***
## time_value 4.619e-05 5.437e-06 8.497 2.47e-16 ***
## EmergDec -4.785e-01 2.611e-01 -1.833 0.0675 .
## time_value:EmergDec 2.427e-05 1.419e-05 1.711 0.0878 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01205 on 480 degrees of freedom
## Multiple R-squared: 0.3631, Adjusted R-squared: 0.3591
## F-statistic: 91.22 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065574 -0.010525 0.001613 0.013264 0.044312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.545e+00 1.623e-01 -9.521 < 2e-16 ***
## time_value 8.962e-05 8.958e-06 10.005 < 2e-16 ***
## EmergDec -1.244e+00 4.456e-01 -2.792 0.00545 **
## time_value:EmergDec 6.438e-05 2.421e-05 2.659 0.00809 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02014 on 480 degrees of freedom
## Multiple R-squared: 0.4046, Adjusted R-squared: 0.4009
## F-statistic: 108.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072061 -0.011632 0.001611 0.013299 0.042020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.678e+00 1.597e-01 -10.504 < 2e-16 ***
## time_value 9.711e-05 8.815e-06 11.017 < 2e-16 ***
## EmergDec -2.367e+00 4.386e-01 -5.396 1.07e-07 ***
## time_value:EmergDec 1.252e-04 2.382e-05 5.255 2.23e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01982 on 480 degrees of freedom
## Multiple R-squared: 0.4383, Adjusted R-squared: 0.4348
## F-statistic: 124.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065828 -0.007026 0.001972 0.010853 0.034875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.029e-01 1.349e-01 -6.695 6.05e-11 ***
## time_value 5.420e-05 7.445e-06 7.280 1.37e-12 ***
## EmergDec -1.033e+00 3.704e-01 -2.789 0.00549 **
## time_value:EmergDec 5.337e-05 2.012e-05 2.653 0.00825 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01674 on 480 degrees of freedom
## Multiple R-squared: 0.4756, Adjusted R-squared: 0.4723
## F-statistic: 145.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073027 -0.007740 0.001762 0.012351 0.076899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.072e+00 1.567e-01 -6.841 2.40e-11 ***
## time_value 6.379e-05 8.652e-06 7.373 7.39e-13 ***
## EmergDec 2.691e-03 4.108e-01 0.007 0.995
## time_value:EmergDec -3.384e-06 2.232e-05 -0.152 0.880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01909 on 480 degrees of freedom
## Multiple R-squared: 0.5142, Adjusted R-squared: 0.5112
## F-statistic: 169.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034822 -0.004185 0.000517 0.004812 0.021433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.030e-01 6.602e-02 -9.134 <2e-16 ***
## time_value 3.640e-05 3.645e-06 9.986 <2e-16 ***
## EmergDec 3.725e-02 1.771e-01 0.210 0.834
## time_value:EmergDec -3.134e-06 9.622e-06 -0.326 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008118 on 480 degrees of freedom
## Multiple R-squared: 0.3225, Adjusted R-squared: 0.3183
## F-statistic: 76.16 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038947 -0.003292 0.000493 0.005168 0.027532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.144e-01 6.684e-02 -9.192 <2e-16 ***
## time_value 3.687e-05 3.690e-06 9.992 <2e-16 ***
## EmergDec -4.152e-01 1.814e-01 -2.289 0.0225 *
## time_value:EmergDec 2.131e-05 9.855e-06 2.162 0.0311 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008258 on 480 degrees of freedom
## Multiple R-squared: 0.3749, Adjusted R-squared: 0.371
## F-statistic: 95.95 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057860 -0.007326 0.000520 0.010444 0.064212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.026e+00 1.298e-01 -7.902 1.89e-14 ***
## time_value 6.066e-05 7.166e-06 8.465 3.13e-16 ***
## EmergDec -1.446e-01 3.363e-01 -0.430 0.667
## time_value:EmergDec 5.410e-06 1.828e-05 0.296 0.767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01573 on 480 degrees of freedom
## Multiple R-squared: 0.4234, Adjusted R-squared: 0.4198
## F-statistic: 117.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061329 -0.006844 0.001102 0.009764 0.071087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.211e+00 1.290e-01 -9.383 <2e-16 ***
## time_value 7.080e-05 7.123e-06 9.939 <2e-16 ***
## EmergDec -6.712e-01 3.381e-01 -1.985 0.0477 *
## time_value:EmergDec 3.392e-05 1.838e-05 1.846 0.0655 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01571 on 480 degrees of freedom
## Multiple R-squared: 0.4158, Adjusted R-squared: 0.4122
## F-statistic: 113.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053155 -0.007580 0.000318 0.008472 0.032373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.208e+00 1.153e-01 -10.481 < 2e-16 ***
## time_value 7.043e-05 6.362e-06 11.070 < 2e-16 ***
## EmergDec -1.351e+00 3.165e-01 -4.268 2.37e-05 ***
## time_value:EmergDec 7.099e-05 1.720e-05 4.129 4.30e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01431 on 480 degrees of freedom
## Multiple R-squared: 0.4276, Adjusted R-squared: 0.424
## F-statistic: 119.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043768 -0.004799 -0.000052 0.006393 0.039984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.080e-01 8.904e-02 -5.705 2.04e-08 ***
## time_value 3.136e-05 4.916e-06 6.378 4.22e-10 ***
## EmergDec 2.896e-01 2.307e-01 1.255 0.210
## time_value:EmergDec -1.706e-05 1.254e-05 -1.361 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01079 on 480 degrees of freedom
## Multiple R-squared: 0.3399, Adjusted R-squared: 0.3357
## F-statistic: 82.37 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058192 -0.006280 0.000813 0.008946 0.062638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.211e+00 1.212e-01 -9.998 <2e-16 ***
## time_value 7.085e-05 6.690e-06 10.590 <2e-16 ***
## EmergDec -1.474e-01 3.140e-01 -0.470 0.639
## time_value:EmergDec 5.436e-06 1.707e-05 0.319 0.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01468 on 480 degrees of freedom
## Multiple R-squared: 0.4621, Adjusted R-squared: 0.4588
## F-statistic: 137.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055398 -0.006020 0.001285 0.009566 0.054827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.074e+00 1.221e-01 -8.797 <2e-16 ***
## time_value 6.324e-05 6.739e-06 9.384 <2e-16 ***
## EmergDec 2.574e-01 3.199e-01 0.805 0.421
## time_value:EmergDec -1.634e-05 1.739e-05 -0.940 0.348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01487 on 480 degrees of freedom
## Multiple R-squared: 0.4248, Adjusted R-squared: 0.4212
## F-statistic: 118.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049716 -0.005089 0.001431 0.007442 0.020226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.036e-01 8.784e-02 -9.149 < 2e-16 ***
## time_value 4.772e-05 4.848e-06 9.842 < 2e-16 ***
## EmergDec -1.229e+00 2.412e-01 -5.097 4.98e-07 ***
## time_value:EmergDec 6.514e-05 1.310e-05 4.971 9.27e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0109 on 480 degrees of freedom
## Multiple R-squared: 0.3815, Adjusted R-squared: 0.3777
## F-statistic: 98.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05695 -0.01155 0.00136 0.01159 0.04521
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.573e+00 1.374e-01 -11.446 < 2e-16 ***
## time_value 9.089e-05 7.586e-06 11.981 < 2e-16 ***
## EmergDec -1.685e+00 3.774e-01 -4.464 1.01e-05 ***
## time_value:EmergDec 8.870e-05 2.050e-05 4.326 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01706 on 480 degrees of freedom
## Multiple R-squared: 0.4158, Adjusted R-squared: 0.4122
## F-statistic: 113.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052959 -0.006125 0.000621 0.008436 0.027540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.003e+00 1.075e-01 -9.331 < 2e-16 ***
## time_value 5.908e-05 5.931e-06 9.960 < 2e-16 ***
## EmergDec -1.302e+00 2.916e-01 -4.466 9.93e-06 ***
## time_value:EmergDec 6.854e-05 1.584e-05 4.326 1.84e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01327 on 480 degrees of freedom
## Multiple R-squared: 0.4342, Adjusted R-squared: 0.4307
## F-statistic: 122.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051130 -0.007129 0.000895 0.007875 0.029710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.120e+00 1.117e-01 -10.025 < 2e-16 ***
## time_value 6.557e-05 6.167e-06 10.632 < 2e-16 ***
## EmergDec -1.207e+00 3.068e-01 -3.933 9.63e-05 ***
## time_value:EmergDec 6.300e-05 1.667e-05 3.780 0.000177 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01387 on 480 degrees of freedom
## Multiple R-squared: 0.4869, Adjusted R-squared: 0.4837
## F-statistic: 151.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054030 -0.005664 0.000970 0.007785 0.045544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.057e-01 1.048e-01 -5.778 1.36e-08 ***
## time_value 3.736e-05 5.788e-06 6.455 2.65e-10 ***
## EmergDec -4.107e-01 2.717e-01 -1.512 0.131
## time_value:EmergDec 2.072e-05 1.477e-05 1.403 0.161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01271 on 480 degrees of freedom
## Multiple R-squared: 0.3272, Adjusted R-squared: 0.323
## F-statistic: 77.82 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056788 -0.004715 0.000784 0.007779 0.029288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.798e-01 1.041e-01 -9.410 <2e-16 ***
## time_value 5.797e-05 5.748e-06 10.086 <2e-16 ***
## EmergDec -3.986e-01 2.826e-01 -1.410 0.159
## time_value:EmergDec 1.920e-05 1.535e-05 1.251 0.212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01286 on 480 degrees of freedom
## Multiple R-squared: 0.5269, Adjusted R-squared: 0.5239
## F-statistic: 178.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047778 -0.006037 0.000132 0.008145 0.036903
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.169e+00 1.057e-01 -11.059 <2e-16 ***
## time_value 6.841e-05 5.834e-06 11.726 <2e-16 ***
## EmergDec -4.472e-01 2.902e-01 -1.541 0.124
## time_value:EmergDec 2.202e-05 1.577e-05 1.396 0.163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01312 on 480 degrees of freedom
## Multiple R-squared: 0.4394, Adjusted R-squared: 0.4359
## F-statistic: 125.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045685 -0.005203 0.000393 0.006650 0.031587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.427e-01 1.006e-01 -5.397 1.07e-07 ***
## time_value 3.345e-05 5.553e-06 6.023 3.41e-09 ***
## EmergDec 2.419e-01 2.463e-01 0.982 0.327
## time_value:EmergDec -1.473e-05 1.340e-05 -1.099 0.272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0119 on 480 degrees of freedom
## Multiple R-squared: 0.392, Adjusted R-squared: 0.3882
## F-statistic: 103.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069205 -0.006522 0.000541 0.010993 0.051191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.787e+00 1.480e-01 -12.071 <2e-16 ***
## time_value 1.032e-04 8.171e-06 12.629 <2e-16 ***
## EmergDec -1.644e-01 4.017e-01 -0.409 0.683
## time_value:EmergDec 5.279e-06 2.182e-05 0.242 0.809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01829 on 480 degrees of freedom
## Multiple R-squared: 0.5233, Adjusted R-squared: 0.5204
## F-statistic: 175.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052329 -0.005619 0.000745 0.008657 0.028779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.313e+00 1.067e-01 -12.304 <2e-16 ***
## time_value 7.620e-05 5.889e-06 12.939 <2e-16 ***
## EmergDec -6.636e-01 2.965e-01 -2.238 0.0257 *
## time_value:EmergDec 3.366e-05 1.610e-05 2.090 0.0371 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01331 on 480 degrees of freedom
## Multiple R-squared: 0.4479, Adjusted R-squared: 0.4445
## F-statistic: 129.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041434 -0.006488 0.000636 0.007863 0.028459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.507e-01 9.155e-02 -8.200 2.22e-15 ***
## time_value 4.477e-05 5.054e-06 8.858 < 2e-16 ***
## EmergDec -1.383e+00 2.514e-01 -5.500 6.18e-08 ***
## time_value:EmergDec 7.363e-05 1.366e-05 5.391 1.10e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01136 on 480 degrees of freedom
## Multiple R-squared: 0.325, Adjusted R-squared: 0.3208
## F-statistic: 77.05 on 3 and 480 DF, p-value: < 2.2e-16School Closure
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*SchoolClose,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.029387 -0.007192 -0.001397 0.006745 0.040948
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.159e-01 5.160e-02 -4.184 3.40e-05 ***
## time_value 1.495e-05 2.836e-06 5.269 2.07e-07 ***
## SchoolClose -9.141e-02 5.343e-01 -0.171 0.864
## time_value:SchoolClose 4.434e-06 2.904e-05 0.153 0.879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01098 on 480 degrees of freedom
## Multiple R-squared: 0.1056, Adjusted R-squared: 0.1001
## F-statistic: 18.9 on 3 and 480 DF, p-value: 1.333e-11
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052541 -0.012439 0.000054 0.011874 0.055633
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.107e-01 8.361e-02 -1.324 0.1862
## time_value 9.976e-06 4.592e-06 2.172 0.0303 *
## SchoolClose -1.925e+00 1.359e+00 -1.416 0.1575
## time_value:SchoolClose 1.031e-04 7.393e-05 1.394 0.1639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01859 on 480 degrees of freedom
## Multiple R-squared: 0.2252, Adjusted R-squared: 0.2204
## F-statistic: 46.51 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058025 -0.009053 0.000617 0.010559 0.037918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.984e-01 7.625e-02 -5.225 2.60e-07 ***
## time_value 2.572e-05 4.195e-06 6.132 1.82e-09 ***
## SchoolClose -7.179e-01 5.690e-01 -1.262 0.208
## time_value:SchoolClose 3.739e-05 3.090e-05 1.210 0.227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01526 on 480 degrees of freedom
## Multiple R-squared: 0.3361, Adjusted R-squared: 0.332
## F-statistic: 81.01 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040540 -0.007015 0.000105 0.006867 0.039910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.818e-02 5.543e-02 1.049 0.2945
## time_value 1.357e-08 3.049e-06 0.004 0.9965
## SchoolClose -9.511e-01 4.433e-01 -2.145 0.0324 *
## time_value:SchoolClose 5.075e-05 2.408e-05 2.108 0.0356 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01129 on 480 degrees of freedom
## Multiple R-squared: 0.2839, Adjusted R-squared: 0.2795
## F-statistic: 63.44 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044702 -0.003603 0.000663 0.005449 0.019723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.811e-01 7.641e-02 -4.987 8.57e-07 ***
## time_value 2.439e-05 4.218e-06 5.782 1.33e-08 ***
## SchoolClose -5.693e-01 2.098e-01 -2.713 0.00691 **
## time_value:SchoolClose 2.973e-05 1.140e-05 2.608 0.00938 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.009484 on 480 degrees of freedom
## Multiple R-squared: 0.3393, Adjusted R-squared: 0.3352
## F-statistic: 82.16 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049561 -0.008113 -0.000521 0.007202 0.039113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.982e-01 6.491e-02 3.054 0.00238 **
## time_value -7.207e-06 3.567e-06 -2.021 0.04386 *
## SchoolClose -1.989e+00 8.037e-01 -2.475 0.01367 *
## time_value:SchoolClose 1.070e-04 4.367e-05 2.449 0.01468 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01402 on 480 degrees of freedom
## Multiple R-squared: 0.28, Adjusted R-squared: 0.2755
## F-statistic: 62.21 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060188 -0.013381 -0.001157 0.011477 0.048898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.858e-01 9.327e-02 4.137 4.16e-05 ***
## time_value -1.712e-05 5.126e-06 -3.340 0.000902 ***
## SchoolClose -1.172e+00 9.813e-01 -1.194 0.232906
## time_value:SchoolClose 6.207e-05 5.333e-05 1.164 0.245006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01986 on 480 degrees of freedom
## Multiple R-squared: 0.3209, Adjusted R-squared: 0.3167
## F-statistic: 75.61 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045323 -0.004851 -0.000196 0.006383 0.029647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.782e-01 9.192e-02 -9.554 <2e-16 ***
## time_value 5.193e-05 5.074e-06 10.235 <2e-16 ***
## SchoolClose 2.967e-02 2.554e-01 0.116 0.908
## time_value:SchoolClose -3.550e-06 1.387e-05 -0.256 0.798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01146 on 480 degrees of freedom
## Multiple R-squared: 0.4531, Adjusted R-squared: 0.4496
## F-statistic: 132.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054892 -0.005178 0.000583 0.008168 0.026662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.021e+00 1.051e-01 -9.710 <2e-16 ***
## time_value 6.016e-05 5.803e-06 10.367 <2e-16 ***
## SchoolClose -1.820e-01 2.887e-01 -0.630 0.529
## time_value:SchoolClose 7.444e-06 1.568e-05 0.475 0.635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01305 on 480 degrees of freedom
## Multiple R-squared: 0.5142, Adjusted R-squared: 0.5112
## F-statistic: 169.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042623 -0.005938 -0.000385 0.006865 0.027115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.260e-01 5.382e-02 -2.342 0.01961 *
## time_value 1.023e-05 2.958e-06 3.459 0.00059 ***
## SchoolClose -1.003e+00 5.273e-01 -1.902 0.05772 .
## time_value:SchoolClose 5.339e-05 2.865e-05 1.863 0.06303 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01134 on 480 degrees of freedom
## Multiple R-squared: 0.3159, Adjusted R-squared: 0.3117
## F-statistic: 73.89 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048408 -0.008381 0.000482 0.008872 0.033866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.507e-02 6.559e-02 -0.230 0.8184
## time_value 4.316e-06 3.604e-06 1.198 0.2316
## SchoolClose -1.438e+00 8.149e-01 -1.764 0.0783 .
## time_value:SchoolClose 7.679e-05 4.429e-05 1.734 0.0836 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01424 on 480 degrees of freedom
## Multiple R-squared: 0.3, Adjusted R-squared: 0.2956
## F-statistic: 68.56 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0141718 -0.0023483 -0.0000245 0.0023857 0.0136462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.083e-01 2.180e-02 -9.554 <2e-16 ***
## time_value 1.314e-05 1.200e-06 10.949 <2e-16 ***
## SchoolClose -2.507e-01 1.196e-01 -2.096 0.0366 *
## time_value:SchoolClose 1.336e-05 6.494e-06 2.056 0.0403 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.004 on 480 degrees of freedom
## Multiple R-squared: 0.2309, Adjusted R-squared: 0.2261
## F-statistic: 48.04 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075534 -0.010726 0.002431 0.014273 0.041630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.773e+00 1.673e-01 -10.594 < 2e-16 ***
## time_value 1.026e-04 9.237e-06 11.107 < 2e-16 ***
## SchoolClose -2.323e+00 4.650e-01 -4.995 8.23e-07 ***
## time_value:SchoolClose 1.224e-04 2.526e-05 4.846 1.70e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02087 on 480 degrees of freedom
## Multiple R-squared: 0.4724, Adjusted R-squared: 0.4691
## F-statistic: 143.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043917 -0.007158 -0.000260 0.008057 0.032299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.335e-02 5.593e-02 -1.133 0.2579
## time_value 6.916e-06 3.073e-06 2.251 0.0248 *
## SchoolClose -1.218e+00 7.973e-01 -1.528 0.1272
## time_value:SchoolClose 6.530e-05 4.334e-05 1.507 0.1325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01226 on 480 degrees of freedom
## Multiple R-squared: 0.1874, Adjusted R-squared: 0.1823
## F-statistic: 36.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05767 -0.01630 -0.00276 0.01088 0.06217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.651e-01 1.018e-01 4.569 6.25e-06 ***
## time_value -2.157e-05 5.591e-06 -3.858 0.00013 ***
## SchoolClose -1.130e+00 1.622e+00 -0.697 0.48639
## time_value:SchoolClose 5.976e-05 8.819e-05 0.678 0.49829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02263 on 480 degrees of freedom
## Multiple R-squared: 0.2514, Adjusted R-squared: 0.2467
## F-statistic: 53.72 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045495 -0.009193 0.000346 0.008432 0.037286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.031e-02 6.712e-02 -0.303 0.762
## time_value 4.608e-06 3.687e-06 1.250 0.212
## SchoolClose -1.396e+00 1.043e+00 -1.339 0.181
## time_value:SchoolClose 7.457e-05 5.669e-05 1.315 0.189
##
## Residual standard error: 0.01486 on 480 degrees of freedom
## Multiple R-squared: 0.257, Adjusted R-squared: 0.2524
## F-statistic: 55.35 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055151 -0.018250 -0.000172 0.012774 0.053827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.978e-01 9.244e-02 2.140 0.0329 *
## time_value -6.997e-06 5.074e-06 -1.379 0.1685
## SchoolClose 6.297e-01 2.642e+00 0.238 0.8117
## time_value:SchoolClose -3.588e-05 1.438e-04 -0.249 0.8031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02117 on 480 degrees of freedom
## Multiple R-squared: 0.1678, Adjusted R-squared: 0.1626
## F-statistic: 32.26 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058301 -0.007043 0.001668 0.009945 0.030559
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.267e+00 1.221e-01 -10.377 <2e-16 ***
## time_value 7.388e-05 6.740e-06 10.960 <2e-16 ***
## SchoolClose -7.064e-01 3.393e-01 -2.082 0.0379 *
## time_value:SchoolClose 3.568e-05 1.843e-05 1.936 0.0535 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01523 on 480 degrees of freedom
## Multiple R-squared: 0.4599, Adjusted R-squared: 0.4565
## F-statistic: 136.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044006 -0.010660 -0.000235 0.010802 0.042149
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.033e-01 7.033e-02 -1.469 0.1424
## time_value 9.353e-06 3.863e-06 2.421 0.0158 *
## SchoolClose -1.184e+00 1.098e+00 -1.078 0.2815
## time_value:SchoolClose 6.312e-05 5.972e-05 1.057 0.2910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01564 on 480 degrees of freedom
## Multiple R-squared: 0.2068, Adjusted R-squared: 0.2019
## F-statistic: 41.73 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050097 -0.012553 -0.000258 0.009881 0.045226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.081e-01 8.935e-02 3.448 0.000614 ***
## time_value -1.294e-05 4.914e-06 -2.634 0.008715 **
## SchoolClose -1.317e+00 7.246e-01 -1.818 0.069751 .
## time_value:SchoolClose 7.004e-05 3.936e-05 1.779 0.075792 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01821 on 480 degrees of freedom
## Multiple R-squared: 0.3382, Adjusted R-squared: 0.3341
## F-statistic: 81.76 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053570 -0.011297 -0.001322 0.008608 0.043765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.491e-01 8.256e-02 5.440 8.51e-08 ***
## time_value -2.092e-05 4.537e-06 -4.611 5.15e-06 ***
## SchoolClose -1.112e+00 9.025e-01 -1.232 0.219
## time_value:SchoolClose 5.905e-05 4.905e-05 1.204 0.229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0177 on 480 degrees of freedom
## Multiple R-squared: 0.323, Adjusted R-squared: 0.3188
## F-statistic: 76.34 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041539 -0.011128 -0.000579 0.008753 0.041545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.203e-01 7.247e-02 1.660 0.0975 .
## time_value -2.977e-06 3.982e-06 -0.748 0.4551
## SchoolClose -1.108e+00 8.867e-01 -1.249 0.2121
## time_value:SchoolClose 5.912e-05 4.816e-05 1.228 0.2202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01548 on 480 degrees of freedom
## Multiple R-squared: 0.1993, Adjusted R-squared: 0.1943
## F-statistic: 39.83 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042742 -0.011232 -0.000908 0.008865 0.040113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.397e-01 7.282e-02 1.918 0.0557 .
## time_value -4.133e-06 4.000e-06 -1.033 0.3020
## SchoolClose -1.127e+00 1.089e+00 -1.035 0.3012
## time_value:SchoolClose 5.996e-05 5.920e-05 1.013 0.3116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01613 on 480 degrees of freedom
## Multiple R-squared: 0.2444, Adjusted R-squared: 0.2396
## F-statistic: 51.74 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055146 -0.016771 -0.002207 0.012141 0.056822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.028e-01 1.074e-01 2.820 0.0050 **
## time_value -1.251e-05 5.902e-06 -2.120 0.0345 *
## SchoolClose -5.462e-01 1.088e+00 -0.502 0.6158
## time_value:SchoolClose 2.792e-05 5.909e-05 0.472 0.6368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02271 on 480 degrees of freedom
## Multiple R-squared: 0.2815, Adjusted R-squared: 0.277
## F-statistic: 62.69 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053284 -0.013614 -0.000462 0.012245 0.052748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.895e-03 9.282e-02 0.085 0.932
## time_value 3.732e-06 5.099e-06 0.732 0.465
## SchoolClose -1.470e+00 1.244e+00 -1.182 0.238
## time_value:SchoolClose 7.810e-05 6.761e-05 1.155 0.249
##
## Residual standard error: 0.02023 on 480 degrees of freedom
## Multiple R-squared: 0.2694, Adjusted R-squared: 0.2648
## F-statistic: 59 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046770 -0.013824 0.000808 0.011326 0.039927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.552e-02 7.197e-02 -0.910 0.3631
## time_value 7.245e-06 3.950e-06 1.834 0.0673 .
## SchoolClose 2.843e-01 1.936e+00 0.147 0.8833
## time_value:SchoolClose -1.681e-05 1.053e-04 -0.160 0.8733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01643 on 480 degrees of freedom
## Multiple R-squared: 0.1725, Adjusted R-squared: 0.1674
## F-statistic: 33.36 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038259 -0.009446 -0.000878 0.008899 0.033757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.767e-02 5.631e-02 1.024 0.306
## time_value 4.279e-07 3.090e-06 0.139 0.890
## SchoolClose 1.635e+00 2.438e+00 0.671 0.503
## time_value:SchoolClose -8.973e-05 1.328e-04 -0.676 0.499
##
## Residual standard error: 0.01304 on 480 degrees of freedom
## Multiple R-squared: 0.06287, Adjusted R-squared: 0.05701
## F-statistic: 10.73 on 3 and 480 DF, p-value: 7.723e-07
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050792 -0.004916 0.001308 0.007968 0.025523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.612e-01 9.401e-02 -8.097 4.69e-15 ***
## time_value 4.546e-05 5.189e-06 8.760 < 2e-16 ***
## SchoolClose -8.276e-01 2.612e-01 -3.168 0.00163 **
## time_value:SchoolClose 4.316e-05 1.419e-05 3.042 0.00248 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01172 on 480 degrees of freedom
## Multiple R-squared: 0.3971, Adjusted R-squared: 0.3933
## F-statistic: 105.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053161 -0.016214 0.000208 0.012407 0.059539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.427e-02 1.060e-01 -0.135 0.893
## time_value 4.920e-06 5.826e-06 0.845 0.399
## SchoolClose 8.064e-01 1.272e+00 0.634 0.526
## time_value:SchoolClose -4.562e-05 6.916e-05 -0.660 0.510
##
## Residual standard error: 0.02299 on 480 degrees of freedom
## Multiple R-squared: 0.2245, Adjusted R-squared: 0.2197
## F-statistic: 46.33 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060507 -0.014843 0.000122 0.012416 0.054988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.553e-01 1.040e-01 -2.455 0.01444 *
## time_value 1.839e-05 5.713e-06 3.220 0.00137 **
## SchoolClose -5.376e-01 1.468e+00 -0.366 0.71429
## time_value:SchoolClose 2.704e-05 7.973e-05 0.339 0.73462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02258 on 480 degrees of freedom
## Multiple R-squared: 0.2714, Adjusted R-squared: 0.2668
## F-statistic: 59.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054542 -0.013232 -0.000768 0.009978 0.049519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.476e-01 8.884e-02 5.038 6.66e-07 ***
## time_value -2.053e-05 4.881e-06 -4.206 3.11e-05 ***
## SchoolClose -4.552e-01 1.130e+00 -0.403 0.687
## time_value:SchoolClose 2.325e-05 6.145e-05 0.378 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01938 on 480 degrees of freedom
## Multiple R-squared: 0.297, Adjusted R-squared: 0.2926
## F-statistic: 67.59 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063620 -0.014116 -0.000337 0.009898 0.051845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.155e-01 1.042e-01 4.950 1.03e-06 ***
## time_value -2.405e-05 5.727e-06 -4.198 3.21e-05 ***
## SchoolClose -1.876e+00 8.863e-01 -2.117 0.0348 *
## time_value:SchoolClose 1.000e-04 4.814e-05 2.078 0.0383 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02138 on 480 degrees of freedom
## Multiple R-squared: 0.3902, Adjusted R-squared: 0.3864
## F-statistic: 102.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034683 -0.004201 0.000585 0.004763 0.021589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.916e-01 6.438e-02 -9.189 <2e-16 ***
## time_value 3.576e-05 3.554e-06 10.064 <2e-16 ***
## SchoolClose -1.043e-01 1.789e-01 -0.583 0.560
## time_value:SchoolClose 4.534e-06 9.717e-06 0.467 0.641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00803 on 480 degrees of freedom
## Multiple R-squared: 0.3371, Adjusted R-squared: 0.333
## F-statistic: 81.38 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.031865 -0.005712 -0.000806 0.006496 0.025437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.683e-02 4.176e-02 1.361 0.174
## time_value -2.737e-07 2.293e-06 -0.119 0.905
## SchoolClose -1.024e-01 8.599e-01 -0.119 0.905
## time_value:SchoolClose 4.857e-06 4.679e-05 0.104 0.917
##
## Residual standard error: 0.009441 on 480 degrees of freedom
## Multiple R-squared: 0.1829, Adjusted R-squared: 0.1778
## F-statistic: 35.82 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046758 -0.010999 -0.000317 0.008118 0.048501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.500e-01 8.185e-02 1.832 0.0676 .
## time_value -4.375e-06 4.500e-06 -0.972 0.3314
## SchoolClose -1.263e+00 7.462e-01 -1.692 0.0912 .
## time_value:SchoolClose 6.711e-05 4.054e-05 1.655 0.0985 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01702 on 480 degrees of freedom
## Multiple R-squared: 0.325, Adjusted R-squared: 0.3207
## F-statistic: 77.02 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048762 -0.011495 -0.000650 0.009363 0.043714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.072e-01 8.069e-02 1.329 0.185
## time_value -2.121e-06 4.432e-06 -0.479 0.633
## SchoolClose -9.844e-01 1.179e+00 -0.835 0.404
## time_value:SchoolClose 5.204e-05 6.413e-05 0.811 0.418
##
## Residual standard error: 0.01781 on 480 degrees of freedom
## Multiple R-squared: 0.2491, Adjusted R-squared: 0.2444
## F-statistic: 53.08 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047512 -0.010236 -0.000800 0.009311 0.044390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.967e-01 7.533e-02 -2.611 0.009305 **
## time_value 1.447e-05 4.140e-06 3.496 0.000517 ***
## SchoolClose -7.945e-01 7.781e-01 -1.021 0.307751
## time_value:SchoolClose 4.169e-05 4.229e-05 0.986 0.324694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.016 on 480 degrees of freedom
## Multiple R-squared: 0.284, Adjusted R-squared: 0.2795
## F-statistic: 63.47 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037923 -0.007174 -0.001280 0.007092 0.032979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.897e-01 5.342e-02 5.424 9.27e-08 ***
## time_value -1.278e-05 2.934e-06 -4.355 1.63e-05 ***
## SchoolClose -5.788e-01 7.819e-01 -0.740 0.459
## time_value:SchoolClose 3.078e-05 4.252e-05 0.724 0.470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01181 on 480 degrees of freedom
## Multiple R-squared: 0.2097, Adjusted R-squared: 0.2047
## F-statistic: 42.44 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048053 -0.010644 -0.001183 0.007929 0.040683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.208e-01 7.760e-02 2.845 0.00463 **
## time_value -8.400e-06 4.262e-06 -1.971 0.04933 *
## SchoolClose -1.154e+00 1.136e+00 -1.016 0.31013
## time_value:SchoolClose 6.134e-05 6.177e-05 0.993 0.32118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01715 on 480 degrees of freedom
## Multiple R-squared: 0.2662, Adjusted R-squared: 0.2616
## F-statistic: 58.03 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045993 -0.009869 0.000260 0.008021 0.038962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.053e-01 8.529e-02 -1.235 0.2174
## time_value 9.676e-06 4.696e-06 2.061 0.0399 *
## SchoolClose -7.904e-01 4.917e-01 -1.608 0.1086
## time_value:SchoolClose 4.139e-05 2.669e-05 1.550 0.1217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01592 on 480 degrees of freedom
## Multiple R-squared: 0.3403, Adjusted R-squared: 0.3362
## F-statistic: 82.53 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.042187 -0.007369 0.000117 0.007526 0.027090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.713e-02 5.538e-02 -1.212 0.2261
## time_value 6.961e-06 3.041e-06 2.289 0.0225 *
## SchoolClose -5.708e-01 8.646e-01 -0.660 0.5095
## time_value:SchoolClose 3.004e-05 4.702e-05 0.639 0.5233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0123 on 480 degrees of freedom
## Multiple R-squared: 0.2124, Adjusted R-squared: 0.2074
## F-statistic: 43.14 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046544 -0.018677 -0.000008 0.013254 0.057944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.830e-03 9.263e-02 0.085 0.933
## time_value 3.358e-06 5.082e-06 0.661 0.509
## SchoolClose 4.593e+00 5.417e+00 0.848 0.397
## time_value:SchoolClose -2.515e-04 2.951e-04 -0.852 0.395
##
## Residual standard error: 0.02156 on 480 degrees of freedom
## Multiple R-squared: 0.06707, Adjusted R-squared: 0.06124
## F-statistic: 11.5 on 3 and 480 DF, p-value: 2.708e-07
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046935 -0.008750 -0.000499 0.008998 0.035730
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.285e-02 6.884e-02 -1.349 0.1781
## time_value 8.743e-06 3.784e-06 2.311 0.0213 *
## SchoolClose -8.105e-01 7.001e-01 -1.158 0.2475
## time_value:SchoolClose 4.263e-05 3.804e-05 1.120 0.2631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01461 on 480 degrees of freedom
## Multiple R-squared: 0.3143, Adjusted R-squared: 0.31
## F-statistic: 73.32 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045702 -0.010239 -0.000925 0.008883 0.043204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.617e-02 7.586e-02 0.740 0.459
## time_value 4.790e-07 4.168e-06 0.115 0.909
## SchoolClose -8.445e-01 9.209e-01 -0.917 0.360
## time_value:SchoolClose 4.442e-05 5.005e-05 0.888 0.375
##
## Residual standard error: 0.01635 on 480 degrees of freedom
## Multiple R-squared: 0.2865, Adjusted R-squared: 0.2821
## F-statistic: 64.25 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054076 -0.004883 0.001416 0.007667 0.027202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.095e-01 9.716e-02 -6.273 7.91e-10 ***
## time_value 3.757e-05 5.363e-06 7.006 8.35e-12 ***
## SchoolClose -9.585e-01 2.700e-01 -3.550 0.000423 ***
## time_value:SchoolClose 5.033e-05 1.466e-05 3.432 0.000651 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01212 on 480 degrees of freedom
## Multiple R-squared: 0.3881, Adjusted R-squared: 0.3842
## F-statistic: 101.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047282 -0.009615 -0.001680 0.008019 0.039893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.366e-01 7.177e-02 4.690 3.57e-06 ***
## time_value -1.487e-05 3.943e-06 -3.771 0.000183 ***
## SchoolClose -2.853e-01 9.881e-01 -0.289 0.772899
## time_value:SchoolClose 1.426e-05 5.373e-05 0.265 0.790743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01578 on 480 degrees of freedom
## Multiple R-squared: 0.288, Adjusted R-squared: 0.2836
## F-statistic: 64.73 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044473 -0.009360 -0.000802 0.006527 0.043797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.974e-01 7.554e-02 -3.937 9.49e-05 ***
## time_value 2.021e-05 4.158e-06 4.860 1.59e-06 ***
## SchoolClose -7.342e-01 4.766e-01 -1.540 0.124
## time_value:SchoolClose 3.842e-05 2.588e-05 1.485 0.138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01443 on 480 degrees of freedom
## Multiple R-squared: 0.3217, Adjusted R-squared: 0.3175
## F-statistic: 75.89 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.041134 -0.006416 -0.000508 0.007975 0.031384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.849e-01 5.970e-02 4.772 2.43e-06 ***
## time_value -1.234e-05 3.282e-06 -3.760 0.000191 ***
## SchoolClose -9.264e-01 5.645e-01 -1.641 0.101436
## time_value:SchoolClose 4.935e-05 3.067e-05 1.609 0.108264
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01251 on 480 degrees of freedom
## Multiple R-squared: 0.3272, Adjusted R-squared: 0.323
## F-statistic: 77.83 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057258 -0.017211 -0.002645 0.011417 0.057229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.366e-01 1.050e-01 3.207 0.00143 **
## time_value -1.431e-05 5.764e-06 -2.482 0.01340 *
## SchoolClose -4.351e-01 1.867e+00 -0.233 0.81579
## time_value:SchoolClose 2.195e-05 1.015e-04 0.216 0.82891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02349 on 480 degrees of freedom
## Multiple R-squared: 0.2135, Adjusted R-squared: 0.2086
## F-statistic: 43.43 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052611 -0.005720 0.000652 0.008592 0.028562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.337e+00 1.068e-01 -12.521 <2e-16 ***
## time_value 7.754e-05 5.893e-06 13.157 <2e-16 ***
## SchoolClose -5.968e-01 2.932e-01 -2.035 0.0424 *
## time_value:SchoolClose 3.002e-05 1.593e-05 1.885 0.0600 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01325 on 480 degrees of freedom
## Multiple R-squared: 0.4525, Adjusted R-squared: 0.449
## F-statistic: 132.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034359 -0.009196 0.000541 0.007456 0.033316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.103e-02 5.632e-02 -1.616 0.1067
## time_value 8.249e-06 3.092e-06 2.667 0.0079 **
## SchoolClose -2.036e-01 1.156e+00 -0.176 0.8603
## time_value:SchoolClose 1.015e-05 6.288e-05 0.161 0.8718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0127 on 480 degrees of freedom
## Multiple R-squared: 0.1569, Adjusted R-squared: 0.1516
## F-statistic: 29.78 on 3 and 480 DF, p-value: < 2.2e-16Bar Restriction
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*BarRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.028800 -0.007557 -0.001024 0.006786 0.040765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.160e-01 4.910e-02 -2.362 0.018576 *
## time_value 9.404e-06 2.695e-06 3.489 0.000529 ***
## BarRestrict -7.758e-01 1.543e+00 -0.503 0.615256
## time_value:BarRestrict 4.176e-05 8.396e-05 0.497 0.619123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01127 on 480 degrees of freedom
## Multiple R-squared: 0.05774, Adjusted R-squared: 0.05185
## F-statistic: 9.805 on 3 and 480 DF, p-value: 2.746e-06
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056479 -0.009052 0.001572 0.010658 0.043996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.149e+00 1.311e-01 -8.768 < 2e-16 ***
## time_value 6.743e-05 7.233e-06 9.323 < 2e-16 ***
## BarRestrict -2.277e+00 3.775e-01 -6.031 3.26e-09 ***
## time_value:BarRestrict 1.212e-04 2.050e-05 5.912 6.44e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01658 on 480 degrees of freedom
## Multiple R-squared: 0.3842, Adjusted R-squared: 0.3803
## F-statistic: 99.81 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053542 -0.008548 0.002104 0.009654 0.032701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.792e-01 1.178e-01 -8.315 9.54e-16 ***
## time_value 5.784e-05 6.500e-06 8.899 < 2e-16 ***
## BarRestrict -1.964e+00 3.392e-01 -5.791 1.27e-08 ***
## time_value:BarRestrict 1.046e-04 1.842e-05 5.676 2.39e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0149 on 480 degrees of freedom
## Multiple R-squared: 0.3674, Adjusted R-squared: 0.3634
## F-statistic: 92.91 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040272 -0.005533 0.000849 0.006581 0.034756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.150e-01 8.251e-02 -5.030 6.95e-07 ***
## time_value 2.617e-05 4.552e-06 5.748 1.61e-08 ***
## BarRestrict -9.158e-01 2.624e-01 -3.490 0.000528 ***
## time_value:BarRestrict 4.835e-05 1.424e-05 3.395 0.000742 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01083 on 480 degrees of freedom
## Multiple R-squared: 0.3407, Adjusted R-squared: 0.3366
## F-statistic: 82.69 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044648 -0.003582 0.000649 0.005427 0.019717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.765e-01 7.577e-02 -4.968 9.41e-07 ***
## time_value 2.413e-05 4.183e-06 5.770 1.42e-08 ***
## BarRestrict -6.243e-01 2.106e-01 -2.965 0.00318 **
## time_value:BarRestrict 3.272e-05 1.144e-05 2.860 0.00441 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00945 on 480 degrees of freedom
## Multiple R-squared: 0.3439, Adjusted R-squared: 0.3398
## F-statistic: 83.88 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050643 -0.005454 0.001400 0.007695 0.028215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.435e-01 9.768e-02 -7.611 1.45e-13 ***
## time_value 4.490e-05 5.392e-06 8.327 8.70e-16 ***
## BarRestrict -9.476e-01 2.747e-01 -3.450 0.000610 ***
## time_value:BarRestrict 4.946e-05 1.492e-05 3.315 0.000984 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01224 on 480 degrees of freedom
## Multiple R-squared: 0.4515, Adjusted R-squared: 0.4481
## F-statistic: 131.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063929 -0.006251 0.001585 0.011672 0.033306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.030e+00 1.363e-01 -7.559 2.08e-13 ***
## time_value 6.123e-05 7.524e-06 8.138 3.49e-15 ***
## BarRestrict -9.760e-01 3.788e-01 -2.577 0.0103 *
## time_value:BarRestrict 5.004e-05 2.057e-05 2.432 0.0154 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.017 on 480 degrees of freedom
## Multiple R-squared: 0.5026, Adjusted R-squared: 0.4995
## F-statistic: 161.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045323 -0.004851 -0.000196 0.006383 0.029647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.782e-01 9.192e-02 -9.554 <2e-16 ***
## time_value 5.193e-05 5.074e-06 10.235 <2e-16 ***
## BarRestrict 2.967e-02 2.554e-01 0.116 0.908
## time_value:BarRestrict -3.550e-06 1.387e-05 -0.256 0.798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01146 on 480 degrees of freedom
## Multiple R-squared: 0.4531, Adjusted R-squared: 0.4496
## F-statistic: 132.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054606 -0.005302 0.000660 0.008227 0.026984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.973e-01 1.051e-01 -9.492 <2e-16 ***
## time_value 5.886e-05 5.799e-06 10.150 <2e-16 ***
## BarRestrict -2.477e-01 2.919e-01 -0.849 0.397
## time_value:BarRestrict 1.103e-05 1.586e-05 0.696 0.487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0131 on 480 degrees of freedom
## Multiple R-squared: 0.5103, Adjusted R-squared: 0.5072
## F-statistic: 166.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045271 -0.004973 -0.000148 0.006617 0.024195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.721e-01 6.064e-02 -6.136 1.77e-09 ***
## time_value 2.386e-05 3.340e-06 7.142 3.44e-12 ***
## BarRestrict -1.399e+00 3.048e-01 -4.589 5.68e-06 ***
## time_value:BarRestrict 7.466e-05 1.654e-05 4.513 8.06e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01073 on 480 degrees of freedom
## Multiple R-squared: 0.3877, Adjusted R-squared: 0.3839
## F-statistic: 101.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055165 -0.005753 0.002078 0.008637 0.026370
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.832e-01 1.037e-01 -6.589 1.17e-10 ***
## time_value 4.127e-05 5.722e-06 7.213 2.15e-12 ***
## BarRestrict -2.088e+00 3.098e-01 -6.742 4.51e-11 ***
## time_value:BarRestrict 1.115e-04 1.682e-05 6.630 9.03e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0133 on 480 degrees of freedom
## Multiple R-squared: 0.3891, Adjusted R-squared: 0.3853
## F-statistic: 101.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0154050 -0.0024407 0.0001185 0.0022496 0.0128068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.310e-01 3.096e-02 -10.693 <2e-16 ***
## time_value 1.993e-05 1.709e-06 11.665 <2e-16 ***
## BarRestrict -1.996e-01 8.705e-02 -2.293 0.0223 *
## time_value:BarRestrict 1.045e-05 4.728e-06 2.211 0.0275 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.003879 on 480 degrees of freedom
## Multiple R-squared: 0.2767, Adjusted R-squared: 0.2722
## F-statistic: 61.21 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075037 -0.010824 0.002705 0.014261 0.042034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.732e+00 1.674e-01 -10.347 < 2e-16 ***
## time_value 1.003e-04 9.239e-06 10.859 < 2e-16 ***
## BarRestrict -2.435e+00 4.707e-01 -5.173 3.38e-07 ***
## time_value:BarRestrict 1.285e-04 2.556e-05 5.028 7.03e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02098 on 480 degrees of freedom
## Multiple R-squared: 0.467, Adjusted R-squared: 0.4637
## F-statistic: 140.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049984 -0.005376 0.001215 0.007615 0.028319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.526e-01 8.738e-02 -7.469 3.85e-13 ***
## time_value 3.952e-05 4.822e-06 8.195 2.29e-15 ***
## BarRestrict -1.304e+00 2.643e-01 -4.935 1.11e-06 ***
## time_value:BarRestrict 6.939e-05 1.435e-05 4.836 1.79e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01126 on 480 degrees of freedom
## Multiple R-squared: 0.3147, Adjusted R-squared: 0.3104
## F-statistic: 73.48 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068833 -0.006604 0.000402 0.009557 0.039560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.526e+00 1.418e-01 -10.762 <2e-16 ***
## time_value 8.860e-05 7.826e-06 11.321 <2e-16 ***
## BarRestrict -3.568e-01 3.940e-01 -0.906 0.366
## time_value:BarRestrict 1.585e-05 2.140e-05 0.741 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01768 on 480 degrees of freedom
## Multiple R-squared: 0.5429, Adjusted R-squared: 0.54
## F-statistic: 190 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053580 -0.005948 0.000851 0.008870 0.024848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.048e+00 1.019e-01 -10.283 < 2e-16 ***
## time_value 6.147e-05 5.625e-06 10.929 < 2e-16 ***
## BarRestrict -1.453e+00 2.832e-01 -5.130 4.22e-07 ***
## time_value:BarRestrict 7.667e-05 1.538e-05 4.985 8.67e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01271 on 480 degrees of freedom
## Multiple R-squared: 0.4565, Adjusted R-squared: 0.4531
## F-statistic: 134.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054925 -0.018135 0.000292 0.013309 0.054432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.253e-01 9.314e-02 2.419 0.0159 *
## time_value -8.534e-06 5.111e-06 -1.670 0.0957 .
## BarRestrict -1.372e+00 3.581e+00 -0.383 0.7018
## time_value:BarRestrict 7.302e-05 1.949e-04 0.375 0.7080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02141 on 480 degrees of freedom
## Multiple R-squared: 0.1486, Adjusted R-squared: 0.1433
## F-statistic: 27.94 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058301 -0.007043 0.001668 0.009945 0.030559
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.267e+00 1.221e-01 -10.377 <2e-16 ***
## time_value 7.388e-05 6.740e-06 10.960 <2e-16 ***
## BarRestrict -7.064e-01 3.393e-01 -2.082 0.0379 *
## time_value:BarRestrict 3.568e-05 1.843e-05 1.936 0.0535 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01523 on 480 degrees of freedom
## Multiple R-squared: 0.4599, Adjusted R-squared: 0.4565
## F-statistic: 136.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047842 -0.007477 0.001216 0.008532 0.030066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.104e+00 1.073e-01 -10.280 < 2e-16 ***
## time_value 6.470e-05 5.925e-06 10.920 < 2e-16 ***
## BarRestrict -1.585e+00 3.018e-01 -5.251 2.27e-07 ***
## time_value:BarRestrict 8.392e-05 1.639e-05 5.119 4.45e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01345 on 480 degrees of freedom
## Multiple R-squared: 0.4136, Adjusted R-squared: 0.4099
## F-statistic: 112.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056367 -0.005605 0.001596 0.010648 0.031913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.783e-01 1.280e-01 -6.862 2.11e-11 ***
## time_value 5.269e-05 7.065e-06 7.457 4.17e-13 ***
## BarRestrict -6.967e-01 3.599e-01 -1.936 0.0535 .
## time_value:BarRestrict 3.517e-05 1.955e-05 1.799 0.0726 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01604 on 480 degrees of freedom
## Multiple R-squared: 0.4864, Adjusted R-squared: 0.4832
## F-statistic: 151.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059135 -0.005200 0.000973 0.008760 0.026098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.048e+00 1.134e-01 -9.236 <2e-16 ***
## time_value 6.188e-05 6.261e-06 9.884 <2e-16 ***
## BarRestrict -2.085e-01 3.152e-01 -0.661 0.509
## time_value:BarRestrict 8.504e-06 1.712e-05 0.497 0.620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01415 on 480 degrees of freedom
## Multiple R-squared: 0.5676, Adjusted R-squared: 0.5649
## F-statistic: 210 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045440 -0.007142 0.001429 0.008819 0.035485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.032e-01 1.094e-01 -7.339 9.27e-13 ***
## time_value 4.812e-05 6.040e-06 7.967 1.19e-14 ***
## BarRestrict -7.434e-01 3.114e-01 -2.387 0.0174 *
## time_value:BarRestrict 3.843e-05 1.691e-05 2.272 0.0235 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01378 on 480 degrees of freedom
## Multiple R-squared: 0.3655, Adjusted R-squared: 0.3616
## F-statistic: 92.19 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046868 -0.008809 0.000211 0.009219 0.033294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.075e-01 8.518e-02 -4.784 2.29e-06 ***
## time_value 2.616e-05 4.693e-06 5.574 4.16e-08 ***
## BarRestrict -1.553e+00 3.885e-01 -3.997 7.42e-05 ***
## time_value:BarRestrict 8.259e-05 2.109e-05 3.917 0.000103 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0145 on 480 degrees of freedom
## Multiple R-squared: 0.3888, Adjusted R-squared: 0.385
## F-statistic: 101.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066738 -0.007913 0.000999 0.010781 0.036782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.508e+00 1.481e-01 -10.184 <2e-16 ***
## time_value 8.766e-05 8.173e-06 10.726 <2e-16 ***
## BarRestrict -2.336e-01 4.164e-01 -0.561 0.575
## time_value:BarRestrict 9.168e-06 2.261e-05 0.405 0.685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01856 on 480 degrees of freedom
## Multiple R-squared: 0.5202, Adjusted R-squared: 0.5172
## F-statistic: 173.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063912 -0.009222 0.001518 0.010918 0.037950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.242e+00 1.383e-01 -8.979 < 2e-16 ***
## time_value 7.288e-05 7.633e-06 9.547 < 2e-16 ***
## BarRestrict -2.190e+00 4.082e-01 -5.365 1.26e-07 ***
## time_value:BarRestrict 1.160e-04 2.216e-05 5.233 2.49e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01766 on 480 degrees of freedom
## Multiple R-squared: 0.4431, Adjusted R-squared: 0.4396
## F-statistic: 127.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049184 -0.008018 0.001594 0.010576 0.031150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.042e-01 1.153e-01 -6.976 1.01e-11 ***
## time_value 4.813e-05 6.362e-06 7.565 1.99e-13 ***
## BarRestrict -2.499e+00 3.444e-01 -7.255 1.63e-12 ***
## time_value:BarRestrict 1.338e-04 1.870e-05 7.157 3.10e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01479 on 480 degrees of freedom
## Multiple R-squared: 0.3295, Adjusted R-squared: 0.3254
## F-statistic: 78.64 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046753 -0.006496 0.000979 0.007670 0.028076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.240e-01 8.878e-02 -8.155 3.08e-15 ***
## time_value 4.370e-05 4.900e-06 8.918 < 2e-16 ***
## BarRestrict -8.780e-01 2.589e-01 -3.392 0.000752 ***
## time_value:BarRestrict 4.625e-05 1.406e-05 3.290 0.001075 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01128 on 480 degrees of freedom
## Multiple R-squared: 0.2984, Adjusted R-squared: 0.2941
## F-statistic: 68.06 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050594 -0.005003 0.001314 0.007876 0.025745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.449e-01 9.372e-02 -7.948 1.37e-14 ***
## time_value 4.455e-05 5.173e-06 8.613 < 2e-16 ***
## BarRestrict -8.859e-01 2.635e-01 -3.362 0.000836 ***
## time_value:BarRestrict 4.634e-05 1.431e-05 3.238 0.001288 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01174 on 480 degrees of freedom
## Multiple R-squared: 0.3952, Adjusted R-squared: 0.3914
## F-statistic: 104.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051660 -0.022708 0.000724 0.013814 0.063589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.771e-01 1.070e-01 2.590 0.00988 **
## time_value -1.125e-05 5.872e-06 -1.915 0.05604 .
## BarRestrict 1.543e+00 3.588e+00 0.430 0.66746
## time_value:BarRestrict -8.544e-05 1.953e-04 -0.437 0.66198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02458 on 480 degrees of freedom
## Multiple R-squared: 0.1133, Adjusted R-squared: 0.1077
## F-statistic: 20.44 on 3 and 480 DF, p-value: 1.76e-12
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060202 -0.014087 -0.000188 0.014316 0.051238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.872e-01 1.146e-01 -5.126 4.31e-07 ***
## time_value 3.676e-05 6.305e-06 5.831 1.01e-08 ***
## BarRestrict -3.638e+00 7.936e-01 -4.584 5.83e-06 ***
## time_value:BarRestrict 1.952e-04 4.306e-05 4.533 7.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02179 on 480 degrees of freedom
## Multiple R-squared: 0.3214, Adjusted R-squared: 0.3172
## F-statistic: 75.78 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054643 -0.012124 -0.000030 0.009988 0.046483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.893e-01 9.181e-02 2.062 0.0398 *
## time_value -6.221e-06 5.050e-06 -1.232 0.2186
## BarRestrict -7.629e-01 6.873e-01 -1.110 0.2675
## time_value:BarRestrict 3.974e-05 3.733e-05 1.065 0.2876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01843 on 480 degrees of freedom
## Multiple R-squared: 0.3647, Adjusted R-squared: 0.3607
## F-statistic: 91.85 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072864 -0.005338 0.002238 0.012172 0.035072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.059e+00 1.451e-01 -7.294 1.25e-12 ***
## time_value 6.304e-05 8.011e-06 7.869 2.40e-14 ***
## BarRestrict -8.161e-01 4.033e-01 -2.024 0.0436 *
## time_value:BarRestrict 4.091e-05 2.191e-05 1.868 0.0624 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0181 on 480 degrees of freedom
## Multiple R-squared: 0.5631, Adjusted R-squared: 0.5603
## F-statistic: 206.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034314 -0.004214 0.000618 0.004690 0.022004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.611e-01 6.382e-02 -8.793 <2e-16 ***
## time_value 3.408e-05 3.522e-06 9.675 <2e-16 ***
## BarRestrict -2.029e-01 1.838e-01 -1.104 0.270
## time_value:BarRestrict 9.905e-06 9.982e-06 0.992 0.322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008072 on 480 degrees of freedom
## Multiple R-squared: 0.3301, Adjusted R-squared: 0.326
## F-statistic: 78.86 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038202 -0.003034 0.000651 0.005164 0.016740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.527e-01 6.520e-02 -8.476 2.88e-16 ***
## time_value 3.345e-05 3.599e-06 9.295 < 2e-16 ***
## BarRestrict -6.762e-01 1.901e-01 -3.557 0.000413 ***
## time_value:BarRestrict 3.550e-05 1.032e-05 3.439 0.000635 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008286 on 480 degrees of freedom
## Multiple R-squared: 0.3706, Adjusted R-squared: 0.3667
## F-statistic: 94.21 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057908 -0.006060 0.001672 0.009946 0.034583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.029e+00 1.180e-01 -8.724 < 2e-16 ***
## time_value 6.086e-05 6.512e-06 9.346 < 2e-16 ***
## BarRestrict -9.444e-01 3.278e-01 -2.881 0.00414 **
## time_value:BarRestrict 4.865e-05 1.781e-05 2.733 0.00652 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01471 on 480 degrees of freedom
## Multiple R-squared: 0.4955, Adjusted R-squared: 0.4923
## F-statistic: 157.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061549 -0.005860 0.001785 0.009674 0.029881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.228e+00 1.193e-01 -10.297 < 2e-16 ***
## time_value 7.179e-05 6.585e-06 10.901 < 2e-16 ***
## BarRestrict -1.275e+00 3.276e-01 -3.892 0.000113 ***
## time_value:BarRestrict 6.656e-05 1.780e-05 3.740 0.000206 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01481 on 480 degrees of freedom
## Multiple R-squared: 0.4811, Adjusted R-squared: 0.4779
## F-statistic: 148.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04623 -0.01411 -0.00051 0.01049 0.04842
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.584e-02 7.659e-02 0.990 0.323
## time_value -6.489e-07 4.203e-06 -0.154 0.877
## BarRestrict -8.180e-01 2.734e+00 -0.299 0.765
## time_value:BarRestrict 4.318e-05 1.487e-04 0.290 0.772
##
## Residual standard error: 0.01755 on 480 degrees of freedom
## Multiple R-squared: 0.1383, Adjusted R-squared: 0.1329
## F-statistic: 25.69 on 3 and 480 DF, p-value: 2.002e-15
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043819 -0.004174 0.000192 0.006729 0.027502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.118e-01 8.234e-02 -6.216 1.11e-09 ***
## time_value 3.157e-05 4.545e-06 6.946 1.23e-11 ***
## BarRestrict -2.056e-01 2.315e-01 -0.888 0.375
## time_value:BarRestrict 9.710e-06 1.258e-05 0.772 0.440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01032 on 480 degrees of freedom
## Multiple R-squared: 0.3965, Adjusted R-squared: 0.3927
## F-statistic: 105.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057589 -0.004572 0.001270 0.008861 0.030202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.162e+00 1.103e-01 -10.529 < 2e-16 ***
## time_value 6.809e-05 6.089e-06 11.181 < 2e-16 ***
## BarRestrict -9.951e-01 3.102e-01 -3.208 0.00143 **
## time_value:BarRestrict 5.134e-05 1.685e-05 3.047 0.00244 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01382 on 480 degrees of freedom
## Multiple R-squared: 0.5233, Adjusted R-squared: 0.5203
## F-statistic: 175.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055072 -0.004971 0.001656 0.009485 0.030150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.046e+00 1.136e-01 -9.209 <2e-16 ***
## time_value 6.169e-05 6.268e-06 9.842 <2e-16 ***
## BarRestrict -4.094e-01 3.193e-01 -1.282 0.200
## time_value:BarRestrict 1.976e-05 1.734e-05 1.139 0.255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01423 on 480 degrees of freedom
## Multiple R-squared: 0.4729, Adjusted R-squared: 0.4696
## F-statistic: 143.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049210 -0.005174 0.001488 0.007520 0.020615
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.604e-01 8.675e-02 -8.765 < 2e-16 ***
## time_value 4.532e-05 4.788e-06 9.466 < 2e-16 ***
## BarRestrict -1.411e+00 2.469e-01 -5.714 1.94e-08 ***
## time_value:BarRestrict 7.500e-05 1.341e-05 5.594 3.74e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01092 on 480 degrees of freedom
## Multiple R-squared: 0.3793, Adjusted R-squared: 0.3754
## F-statistic: 97.76 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046188 -0.018887 -0.000812 0.013862 0.058543
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.252e-02 9.332e-02 0.456 0.649
## time_value 1.420e-06 5.119e-06 0.277 0.782
## BarRestrict 7.111e-02 1.422e+01 0.005 0.996
## time_value:BarRestrict -5.237e-06 7.739e-04 -0.007 0.995
##
## Residual standard error: 0.02184 on 480 degrees of freedom
## Multiple R-squared: 0.0426, Adjusted R-squared: 0.03662
## F-statistic: 7.12 on 3 and 480 DF, p-value: 0.0001094
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046962 -0.008172 0.000588 0.009015 0.033104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.218e-01 8.283e-02 -3.886 0.000116 ***
## time_value 2.141e-05 4.563e-06 4.692 3.53e-06 ***
## BarRestrict -2.537e+00 4.078e-01 -6.221 1.08e-09 ***
## time_value:BarRestrict 1.362e-04 2.213e-05 6.156 1.58e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01434 on 480 degrees of freedom
## Multiple R-squared: 0.3398, Adjusted R-squared: 0.3356
## F-statistic: 82.33 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049424 -0.007172 0.001112 0.008001 0.030217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.744e-01 1.128e-01 -8.642 < 2e-16 ***
## time_value 5.749e-05 6.223e-06 9.239 < 2e-16 ***
## BarRestrict -1.539e+00 3.288e-01 -4.682 3.70e-06 ***
## time_value:BarRestrict 8.118e-05 1.785e-05 4.548 6.88e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01433 on 480 degrees of freedom
## Multiple R-squared: 0.4523, Adjusted R-squared: 0.4488
## F-statistic: 132.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053506 -0.004977 0.001596 0.007805 0.027619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.608e-01 9.626e-02 -5.826 1.04e-08 ***
## time_value 3.487e-05 5.313e-06 6.564 1.36e-10 ***
## BarRestrict -1.131e+00 2.773e-01 -4.080 5.28e-05 ***
## time_value:BarRestrict 5.974e-05 1.506e-05 3.967 8.37e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01218 on 480 degrees of freedom
## Multiple R-squared: 0.3821, Adjusted R-squared: 0.3783
## F-statistic: 98.96 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054127 -0.004781 0.001359 0.007662 0.029925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.520e-01 1.049e-01 -7.172 2.81e-12 ***
## time_value 4.535e-05 5.786e-06 7.837 2.99e-14 ***
## BarRestrict -9.646e-01 3.172e-01 -3.041 0.00248 **
## time_value:BarRestrict 5.011e-05 1.722e-05 2.911 0.00377 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01351 on 480 degrees of freedom
## Multiple R-squared: 0.478, Adjusted R-squared: 0.4747
## F-statistic: 146.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047499 -0.006006 0.000174 0.008296 0.037097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.149e+00 1.036e-01 -11.088 <2e-16 ***
## time_value 6.732e-05 5.720e-06 11.769 <2e-16 ***
## BarRestrict -6.404e-01 2.914e-01 -2.198 0.0285 *
## time_value:BarRestrict 3.249e-05 1.583e-05 2.053 0.0406 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01299 on 480 degrees of freedom
## Multiple R-squared: 0.4506, Adjusted R-squared: 0.4472
## F-statistic: 131.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045334 -0.003472 0.000660 0.005934 0.026009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.130e-01 8.931e-02 -5.744 1.64e-08 ***
## time_value 3.180e-05 4.929e-06 6.451 2.72e-10 ***
## BarRestrict -5.873e-01 2.482e-01 -2.367 0.0183 *
## time_value:BarRestrict 3.016e-05 1.348e-05 2.237 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01114 on 480 degrees of freedom
## Multiple R-squared: 0.4669, Adjusted R-squared: 0.4636
## F-statistic: 140.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056987 -0.018275 -0.002854 0.011642 0.059863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.032e-01 1.061e-01 4.742 2.79e-06 ***
## time_value -2.356e-05 5.824e-06 -4.045 6.09e-05 ***
## BarRestrict 2.223e+00 4.088e+00 0.544 0.587
## time_value:BarRestrict -1.226e-04 2.226e-04 -0.551 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02451 on 480 degrees of freedom
## Multiple R-squared: 0.1439, Adjusted R-squared: 0.1386
## F-statistic: 26.9 on 3 and 480 DF, p-value: 4.259e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051911 -0.005789 0.000825 0.008702 0.029125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.277e+00 1.057e-01 -12.082 < 2e-16 ***
## time_value 7.421e-05 5.833e-06 12.723 < 2e-16 ***
## BarRestrict -8.304e-01 3.008e-01 -2.761 0.00598 **
## time_value:BarRestrict 4.273e-05 1.633e-05 2.616 0.00917 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01331 on 480 degrees of freedom
## Multiple R-squared: 0.448, Adjusted R-squared: 0.4446
## F-statistic: 129.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040605 -0.006791 0.000953 0.007909 0.029045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.822e-01 9.094e-02 -7.502 3.06e-13 ***
## time_value 4.097e-05 5.019e-06 8.163 2.89e-15 ***
## BarRestrict -1.567e+00 2.619e-01 -5.983 4.28e-09 ***
## time_value:BarRestrict 8.370e-05 1.422e-05 5.884 7.50e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0115 on 480 degrees of freedom
## Multiple R-squared: 0.3085, Adjusted R-squared: 0.3041
## F-statistic: 71.37 on 3 and 480 DF, p-value: < 2.2e-16Restaurant Restriction
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*RestaurantRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.028800 -0.007557 -0.001024 0.006786 0.040765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.160e-01 4.910e-02 -2.362 0.018576 *
## time_value 9.404e-06 2.695e-06 3.489 0.000529 ***
## RestaurantRestrict -7.758e-01 1.543e+00 -0.503 0.615256
## time_value:RestaurantRestrict 4.176e-05 8.396e-05 0.497 0.619123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01127 on 480 degrees of freedom
## Multiple R-squared: 0.05774, Adjusted R-squared: 0.05185
## F-statistic: 9.805 on 3 and 480 DF, p-value: 2.746e-06
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056479 -0.009052 0.001572 0.010658 0.043996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.149e+00 1.311e-01 -8.768 < 2e-16 ***
## time_value 6.743e-05 7.233e-06 9.323 < 2e-16 ***
## RestaurantRestrict -2.277e+00 3.775e-01 -6.031 3.26e-09 ***
## time_value:RestaurantRestrict 1.212e-04 2.050e-05 5.912 6.44e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01658 on 480 degrees of freedom
## Multiple R-squared: 0.3842, Adjusted R-squared: 0.3803
## F-statistic: 99.81 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053542 -0.008548 0.002104 0.009654 0.032701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.792e-01 1.178e-01 -8.315 9.54e-16 ***
## time_value 5.784e-05 6.500e-06 8.899 < 2e-16 ***
## RestaurantRestrict -1.964e+00 3.392e-01 -5.791 1.27e-08 ***
## time_value:RestaurantRestrict 1.046e-04 1.842e-05 5.676 2.39e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0149 on 480 degrees of freedom
## Multiple R-squared: 0.3674, Adjusted R-squared: 0.3634
## F-statistic: 92.91 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040272 -0.005533 0.000849 0.006581 0.034756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.150e-01 8.251e-02 -5.030 6.95e-07 ***
## time_value 2.617e-05 4.552e-06 5.748 1.61e-08 ***
## RestaurantRestrict -9.158e-01 2.624e-01 -3.490 0.000528 ***
## time_value:RestaurantRestrict 4.835e-05 1.424e-05 3.395 0.000742 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01083 on 480 degrees of freedom
## Multiple R-squared: 0.3407, Adjusted R-squared: 0.3366
## F-statistic: 82.69 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044648 -0.003582 0.000649 0.005427 0.019717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.765e-01 7.577e-02 -4.968 9.41e-07 ***
## time_value 2.413e-05 4.183e-06 5.770 1.42e-08 ***
## RestaurantRestrict -6.243e-01 2.106e-01 -2.965 0.00318 **
## time_value:RestaurantRestrict 3.272e-05 1.144e-05 2.860 0.00441 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00945 on 480 degrees of freedom
## Multiple R-squared: 0.3439, Adjusted R-squared: 0.3398
## F-statistic: 83.88 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050643 -0.005454 0.001400 0.007695 0.028215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.435e-01 9.768e-02 -7.611 1.45e-13 ***
## time_value 4.490e-05 5.392e-06 8.327 8.70e-16 ***
## RestaurantRestrict -9.476e-01 2.747e-01 -3.450 0.000610 ***
## time_value:RestaurantRestrict 4.946e-05 1.492e-05 3.315 0.000984 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01224 on 480 degrees of freedom
## Multiple R-squared: 0.4515, Adjusted R-squared: 0.4481
## F-statistic: 131.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063929 -0.006251 0.001585 0.011672 0.033306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.030e+00 1.363e-01 -7.559 2.08e-13 ***
## time_value 6.123e-05 7.524e-06 8.138 3.49e-15 ***
## RestaurantRestrict -9.760e-01 3.788e-01 -2.577 0.0103 *
## time_value:RestaurantRestrict 5.004e-05 2.057e-05 2.432 0.0154 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.017 on 480 degrees of freedom
## Multiple R-squared: 0.5026, Adjusted R-squared: 0.4995
## F-statistic: 161.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045323 -0.004851 -0.000196 0.006383 0.029647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.782e-01 9.192e-02 -9.554 <2e-16 ***
## time_value 5.193e-05 5.074e-06 10.235 <2e-16 ***
## RestaurantRestrict 2.967e-02 2.554e-01 0.116 0.908
## time_value:RestaurantRestrict -3.550e-06 1.387e-05 -0.256 0.798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01146 on 480 degrees of freedom
## Multiple R-squared: 0.4531, Adjusted R-squared: 0.4496
## F-statistic: 132.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054606 -0.005302 0.000660 0.008227 0.026984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.973e-01 1.051e-01 -9.492 <2e-16 ***
## time_value 5.886e-05 5.799e-06 10.150 <2e-16 ***
## RestaurantRestrict -2.477e-01 2.919e-01 -0.849 0.397
## time_value:RestaurantRestrict 1.103e-05 1.586e-05 0.696 0.487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0131 on 480 degrees of freedom
## Multiple R-squared: 0.5103, Adjusted R-squared: 0.5072
## F-statistic: 166.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045271 -0.004973 -0.000148 0.006617 0.024195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.721e-01 6.064e-02 -6.136 1.77e-09 ***
## time_value 2.386e-05 3.340e-06 7.142 3.44e-12 ***
## RestaurantRestrict -1.399e+00 3.048e-01 -4.589 5.68e-06 ***
## time_value:RestaurantRestrict 7.466e-05 1.654e-05 4.513 8.06e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01073 on 480 degrees of freedom
## Multiple R-squared: 0.3877, Adjusted R-squared: 0.3839
## F-statistic: 101.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052647 -0.006292 0.002000 0.009489 0.029123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.713e-01 1.058e-01 -4.454 1.05e-05 ***
## time_value 2.953e-05 5.838e-06 5.058 6.03e-07 ***
## RestaurantRestrict -2.423e+00 3.498e-01 -6.926 1.40e-11 ***
## time_value:RestaurantRestrict 1.299e-04 1.898e-05 6.843 2.38e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01408 on 480 degrees of freedom
## Multiple R-squared: 0.3153, Adjusted R-squared: 0.311
## F-statistic: 73.68 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0154050 -0.0024407 0.0001185 0.0022496 0.0128068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.310e-01 3.096e-02 -10.693 <2e-16 ***
## time_value 1.993e-05 1.709e-06 11.665 <2e-16 ***
## RestaurantRestrict -1.996e-01 8.705e-02 -2.293 0.0223 *
## time_value:RestaurantRestrict 1.045e-05 4.728e-06 2.211 0.0275 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.003879 on 480 degrees of freedom
## Multiple R-squared: 0.2767, Adjusted R-squared: 0.2722
## F-statistic: 61.21 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075037 -0.010824 0.002705 0.014261 0.042034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.732e+00 1.674e-01 -10.347 < 2e-16 ***
## time_value 1.003e-04 9.239e-06 10.859 < 2e-16 ***
## RestaurantRestrict -2.435e+00 4.707e-01 -5.173 3.38e-07 ***
## time_value:RestaurantRestrict 1.285e-04 2.556e-05 5.028 7.03e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02098 on 480 degrees of freedom
## Multiple R-squared: 0.467, Adjusted R-squared: 0.4637
## F-statistic: 140.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049984 -0.005376 0.001215 0.007615 0.028319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.526e-01 8.738e-02 -7.469 3.85e-13 ***
## time_value 3.952e-05 4.822e-06 8.195 2.29e-15 ***
## RestaurantRestrict -1.304e+00 2.643e-01 -4.935 1.11e-06 ***
## time_value:RestaurantRestrict 6.939e-05 1.435e-05 4.836 1.79e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01126 on 480 degrees of freedom
## Multiple R-squared: 0.3147, Adjusted R-squared: 0.3104
## F-statistic: 73.48 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068833 -0.006604 0.000402 0.009557 0.039560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.526e+00 1.418e-01 -10.762 <2e-16 ***
## time_value 8.860e-05 7.826e-06 11.321 <2e-16 ***
## RestaurantRestrict -3.568e-01 3.940e-01 -0.906 0.366
## time_value:RestaurantRestrict 1.585e-05 2.140e-05 0.741 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01768 on 480 degrees of freedom
## Multiple R-squared: 0.5429, Adjusted R-squared: 0.54
## F-statistic: 190 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053580 -0.005948 0.000851 0.008870 0.024848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.048e+00 1.019e-01 -10.283 < 2e-16 ***
## time_value 6.147e-05 5.625e-06 10.929 < 2e-16 ***
## RestaurantRestrict -1.453e+00 2.832e-01 -5.130 4.22e-07 ***
## time_value:RestaurantRestrict 7.667e-05 1.538e-05 4.985 8.67e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01271 on 480 degrees of freedom
## Multiple R-squared: 0.4565, Adjusted R-squared: 0.4531
## F-statistic: 134.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054925 -0.018135 0.000292 0.013309 0.054432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.253e-01 9.314e-02 2.419 0.0159 *
## time_value -8.534e-06 5.111e-06 -1.670 0.0957 .
## RestaurantRestrict -1.372e+00 3.581e+00 -0.383 0.7018
## time_value:RestaurantRestrict 7.302e-05 1.949e-04 0.375 0.7080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02141 on 480 degrees of freedom
## Multiple R-squared: 0.1486, Adjusted R-squared: 0.1433
## F-statistic: 27.94 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058301 -0.007043 0.001668 0.009945 0.030559
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.267e+00 1.221e-01 -10.377 <2e-16 ***
## time_value 7.388e-05 6.740e-06 10.960 <2e-16 ***
## RestaurantRestrict -7.064e-01 3.393e-01 -2.082 0.0379 *
## time_value:RestaurantRestrict 3.568e-05 1.843e-05 1.936 0.0535 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01523 on 480 degrees of freedom
## Multiple R-squared: 0.4599, Adjusted R-squared: 0.4565
## F-statistic: 136.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047842 -0.007477 0.001216 0.008532 0.030066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.104e+00 1.073e-01 -10.280 < 2e-16 ***
## time_value 6.470e-05 5.925e-06 10.920 < 2e-16 ***
## RestaurantRestrict -1.585e+00 3.018e-01 -5.251 2.27e-07 ***
## time_value:RestaurantRestrict 8.392e-05 1.639e-05 5.119 4.45e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01345 on 480 degrees of freedom
## Multiple R-squared: 0.4136, Adjusted R-squared: 0.4099
## F-statistic: 112.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056367 -0.005605 0.001596 0.010648 0.031913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.783e-01 1.280e-01 -6.862 2.11e-11 ***
## time_value 5.269e-05 7.065e-06 7.457 4.17e-13 ***
## RestaurantRestrict -6.967e-01 3.599e-01 -1.936 0.0535 .
## time_value:RestaurantRestrict 3.517e-05 1.955e-05 1.799 0.0726 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01604 on 480 degrees of freedom
## Multiple R-squared: 0.4864, Adjusted R-squared: 0.4832
## F-statistic: 151.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059135 -0.005200 0.000973 0.008760 0.026098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.048e+00 1.134e-01 -9.236 <2e-16 ***
## time_value 6.188e-05 6.261e-06 9.884 <2e-16 ***
## RestaurantRestrict -2.085e-01 3.152e-01 -0.661 0.509
## time_value:RestaurantRestrict 8.504e-06 1.712e-05 0.497 0.620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01415 on 480 degrees of freedom
## Multiple R-squared: 0.5676, Adjusted R-squared: 0.5649
## F-statistic: 210 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045440 -0.007142 0.001429 0.008819 0.035485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.032e-01 1.094e-01 -7.339 9.27e-13 ***
## time_value 4.812e-05 6.040e-06 7.967 1.19e-14 ***
## RestaurantRestrict -7.434e-01 3.114e-01 -2.387 0.0174 *
## time_value:RestaurantRestrict 3.843e-05 1.691e-05 2.272 0.0235 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01378 on 480 degrees of freedom
## Multiple R-squared: 0.3655, Adjusted R-squared: 0.3616
## F-statistic: 92.19 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046868 -0.008809 0.000211 0.009219 0.033294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.075e-01 8.518e-02 -4.784 2.29e-06 ***
## time_value 2.616e-05 4.693e-06 5.574 4.16e-08 ***
## RestaurantRestrict -1.553e+00 3.885e-01 -3.997 7.42e-05 ***
## time_value:RestaurantRestrict 8.259e-05 2.109e-05 3.917 0.000103 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0145 on 480 degrees of freedom
## Multiple R-squared: 0.3888, Adjusted R-squared: 0.385
## F-statistic: 101.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066738 -0.007913 0.000999 0.010781 0.036782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.508e+00 1.481e-01 -10.184 <2e-16 ***
## time_value 8.766e-05 8.173e-06 10.726 <2e-16 ***
## RestaurantRestrict -2.336e-01 4.164e-01 -0.561 0.575
## time_value:RestaurantRestrict 9.168e-06 2.261e-05 0.405 0.685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01856 on 480 degrees of freedom
## Multiple R-squared: 0.5202, Adjusted R-squared: 0.5172
## F-statistic: 173.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063912 -0.009222 0.001518 0.010918 0.037950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.242e+00 1.383e-01 -8.979 < 2e-16 ***
## time_value 7.288e-05 7.633e-06 9.547 < 2e-16 ***
## RestaurantRestrict -2.190e+00 4.082e-01 -5.365 1.26e-07 ***
## time_value:RestaurantRestrict 1.160e-04 2.216e-05 5.233 2.49e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01766 on 480 degrees of freedom
## Multiple R-squared: 0.4431, Adjusted R-squared: 0.4396
## F-statistic: 127.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049184 -0.008018 0.001594 0.010576 0.031150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.042e-01 1.153e-01 -6.976 1.01e-11 ***
## time_value 4.813e-05 6.362e-06 7.565 1.99e-13 ***
## RestaurantRestrict -2.499e+00 3.444e-01 -7.255 1.63e-12 ***
## time_value:RestaurantRestrict 1.338e-04 1.870e-05 7.157 3.10e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01479 on 480 degrees of freedom
## Multiple R-squared: 0.3295, Adjusted R-squared: 0.3254
## F-statistic: 78.64 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046753 -0.006496 0.000979 0.007670 0.028076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.240e-01 8.878e-02 -8.155 3.08e-15 ***
## time_value 4.370e-05 4.900e-06 8.918 < 2e-16 ***
## RestaurantRestrict -8.780e-01 2.589e-01 -3.392 0.000752 ***
## time_value:RestaurantRestrict 4.625e-05 1.406e-05 3.290 0.001075 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01128 on 480 degrees of freedom
## Multiple R-squared: 0.2984, Adjusted R-squared: 0.2941
## F-statistic: 68.06 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050594 -0.005003 0.001314 0.007876 0.025745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.449e-01 9.372e-02 -7.948 1.37e-14 ***
## time_value 4.455e-05 5.173e-06 8.613 < 2e-16 ***
## RestaurantRestrict -8.859e-01 2.635e-01 -3.362 0.000836 ***
## time_value:RestaurantRestrict 4.634e-05 1.431e-05 3.238 0.001288 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01174 on 480 degrees of freedom
## Multiple R-squared: 0.3952, Adjusted R-squared: 0.3914
## F-statistic: 104.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051660 -0.022708 0.000724 0.013814 0.063589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.771e-01 1.070e-01 2.590 0.00988 **
## time_value -1.125e-05 5.872e-06 -1.915 0.05604 .
## RestaurantRestrict 1.543e+00 3.588e+00 0.430 0.66746
## time_value:RestaurantRestrict -8.544e-05 1.953e-04 -0.437 0.66198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02458 on 480 degrees of freedom
## Multiple R-squared: 0.1133, Adjusted R-squared: 0.1077
## F-statistic: 20.44 on 3 and 480 DF, p-value: 1.76e-12
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060202 -0.014087 -0.000188 0.014316 0.051238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.872e-01 1.146e-01 -5.126 4.31e-07 ***
## time_value 3.676e-05 6.305e-06 5.831 1.01e-08 ***
## RestaurantRestrict -3.638e+00 7.936e-01 -4.584 5.83e-06 ***
## time_value:RestaurantRestrict 1.952e-04 4.306e-05 4.533 7.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02179 on 480 degrees of freedom
## Multiple R-squared: 0.3214, Adjusted R-squared: 0.3172
## F-statistic: 75.78 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.054643 -0.012124 -0.000030 0.009988 0.046483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.893e-01 9.181e-02 2.062 0.0398 *
## time_value -6.221e-06 5.050e-06 -1.232 0.2186
## RestaurantRestrict -7.629e-01 6.873e-01 -1.110 0.2675
## time_value:RestaurantRestrict 3.974e-05 3.733e-05 1.065 0.2876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01843 on 480 degrees of freedom
## Multiple R-squared: 0.3647, Adjusted R-squared: 0.3607
## F-statistic: 91.85 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072864 -0.005338 0.002238 0.012172 0.035072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.059e+00 1.451e-01 -7.294 1.25e-12 ***
## time_value 6.304e-05 8.011e-06 7.869 2.40e-14 ***
## RestaurantRestrict -8.161e-01 4.033e-01 -2.024 0.0436 *
## time_value:RestaurantRestrict 4.091e-05 2.191e-05 1.868 0.0624 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0181 on 480 degrees of freedom
## Multiple R-squared: 0.5631, Adjusted R-squared: 0.5603
## F-statistic: 206.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034314 -0.004214 0.000618 0.004690 0.022004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.611e-01 6.382e-02 -8.793 <2e-16 ***
## time_value 3.408e-05 3.522e-06 9.675 <2e-16 ***
## RestaurantRestrict -2.029e-01 1.838e-01 -1.104 0.270
## time_value:RestaurantRestrict 9.905e-06 9.982e-06 0.992 0.322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008072 on 480 degrees of freedom
## Multiple R-squared: 0.3301, Adjusted R-squared: 0.326
## F-statistic: 78.86 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.038202 -0.003034 0.000651 0.005164 0.016740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.527e-01 6.520e-02 -8.476 2.88e-16 ***
## time_value 3.345e-05 3.599e-06 9.295 < 2e-16 ***
## RestaurantRestrict -6.762e-01 1.901e-01 -3.557 0.000413 ***
## time_value:RestaurantRestrict 3.550e-05 1.032e-05 3.439 0.000635 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008286 on 480 degrees of freedom
## Multiple R-squared: 0.3706, Adjusted R-squared: 0.3667
## F-statistic: 94.21 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057908 -0.006060 0.001672 0.009946 0.034583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.029e+00 1.180e-01 -8.724 < 2e-16 ***
## time_value 6.086e-05 6.512e-06 9.346 < 2e-16 ***
## RestaurantRestrict -9.444e-01 3.278e-01 -2.881 0.00414 **
## time_value:RestaurantRestrict 4.865e-05 1.781e-05 2.733 0.00652 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01471 on 480 degrees of freedom
## Multiple R-squared: 0.4955, Adjusted R-squared: 0.4923
## F-statistic: 157.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061549 -0.005860 0.001785 0.009674 0.029881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.228e+00 1.193e-01 -10.297 < 2e-16 ***
## time_value 7.179e-05 6.585e-06 10.901 < 2e-16 ***
## RestaurantRestrict -1.275e+00 3.276e-01 -3.892 0.000113 ***
## time_value:RestaurantRestrict 6.656e-05 1.780e-05 3.740 0.000206 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01481 on 480 degrees of freedom
## Multiple R-squared: 0.4811, Adjusted R-squared: 0.4779
## F-statistic: 148.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04623 -0.01411 -0.00051 0.01049 0.04842
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.584e-02 7.659e-02 0.990 0.323
## time_value -6.489e-07 4.203e-06 -0.154 0.877
## RestaurantRestrict -8.180e-01 2.734e+00 -0.299 0.765
## time_value:RestaurantRestrict 4.318e-05 1.487e-04 0.290 0.772
##
## Residual standard error: 0.01755 on 480 degrees of freedom
## Multiple R-squared: 0.1383, Adjusted R-squared: 0.1329
## F-statistic: 25.69 on 3 and 480 DF, p-value: 2.002e-15
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043819 -0.004174 0.000192 0.006729 0.027502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.118e-01 8.234e-02 -6.216 1.11e-09 ***
## time_value 3.157e-05 4.545e-06 6.946 1.23e-11 ***
## RestaurantRestrict -2.056e-01 2.315e-01 -0.888 0.375
## time_value:RestaurantRestrict 9.710e-06 1.258e-05 0.772 0.440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01032 on 480 degrees of freedom
## Multiple R-squared: 0.3965, Adjusted R-squared: 0.3927
## F-statistic: 105.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057589 -0.004572 0.001270 0.008861 0.030202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.162e+00 1.103e-01 -10.529 < 2e-16 ***
## time_value 6.809e-05 6.089e-06 11.181 < 2e-16 ***
## RestaurantRestrict -9.951e-01 3.102e-01 -3.208 0.00143 **
## time_value:RestaurantRestrict 5.134e-05 1.685e-05 3.047 0.00244 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01382 on 480 degrees of freedom
## Multiple R-squared: 0.5233, Adjusted R-squared: 0.5203
## F-statistic: 175.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055072 -0.004971 0.001656 0.009485 0.030150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.046e+00 1.136e-01 -9.209 <2e-16 ***
## time_value 6.169e-05 6.268e-06 9.842 <2e-16 ***
## RestaurantRestrict -4.094e-01 3.193e-01 -1.282 0.200
## time_value:RestaurantRestrict 1.976e-05 1.734e-05 1.139 0.255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01423 on 480 degrees of freedom
## Multiple R-squared: 0.4729, Adjusted R-squared: 0.4696
## F-statistic: 143.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045724 -0.006117 0.000785 0.007582 0.023116
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.969e-01 6.643e-02 -5.975 4.49e-09 ***
## time_value 2.522e-05 3.660e-06 6.891 1.75e-11 ***
## RestaurantRestrict -1.787e+00 3.101e-01 -5.765 1.46e-08 ***
## time_value:RestaurantRestrict 9.584e-05 1.683e-05 5.695 2.15e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01135 on 480 degrees of freedom
## Multiple R-squared: 0.3295, Adjusted R-squared: 0.3253
## F-statistic: 78.64 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046188 -0.018887 -0.000812 0.013862 0.058543
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.252e-02 9.332e-02 0.456 0.649
## time_value 1.420e-06 5.119e-06 0.277 0.782
## RestaurantRestrict 7.111e-02 1.422e+01 0.005 0.996
## time_value:RestaurantRestrict -5.237e-06 7.739e-04 -0.007 0.995
##
## Residual standard error: 0.02184 on 480 degrees of freedom
## Multiple R-squared: 0.0426, Adjusted R-squared: 0.03662
## F-statistic: 7.12 on 3 and 480 DF, p-value: 0.0001094
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046168 -0.011619 -0.000176 0.010098 0.039571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.736e-01 7.007e-02 2.477 0.0136 *
## time_value -6.035e-06 3.846e-06 -1.569 0.1172
## RestaurantRestrict -1.069e+00 2.434e+00 -0.439 0.6607
## time_value:RestaurantRestrict 5.692e-05 1.325e-04 0.430 0.6677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01611 on 480 degrees of freedom
## Multiple R-squared: 0.1671, Adjusted R-squared: 0.1619
## F-statistic: 32.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049424 -0.007172 0.001112 0.008001 0.030217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.744e-01 1.128e-01 -8.642 < 2e-16 ***
## time_value 5.749e-05 6.223e-06 9.239 < 2e-16 ***
## RestaurantRestrict -1.539e+00 3.288e-01 -4.682 3.70e-06 ***
## time_value:RestaurantRestrict 8.118e-05 1.785e-05 4.548 6.88e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01433 on 480 degrees of freedom
## Multiple R-squared: 0.4523, Adjusted R-squared: 0.4488
## F-statistic: 132.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053506 -0.004977 0.001596 0.007805 0.027619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.608e-01 9.626e-02 -5.826 1.04e-08 ***
## time_value 3.487e-05 5.313e-06 6.564 1.36e-10 ***
## RestaurantRestrict -1.131e+00 2.773e-01 -4.080 5.28e-05 ***
## time_value:RestaurantRestrict 5.974e-05 1.506e-05 3.967 8.37e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01218 on 480 degrees of freedom
## Multiple R-squared: 0.3821, Adjusted R-squared: 0.3783
## F-statistic: 98.96 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056096 -0.004643 0.001141 0.007281 0.028415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.210e-01 1.030e-01 -8.946 <2e-16 ***
## time_value 5.471e-05 5.682e-06 9.628 <2e-16 ***
## RestaurantRestrict -6.375e-01 2.895e-01 -2.202 0.0281 *
## time_value:RestaurantRestrict 3.220e-05 1.572e-05 2.048 0.0411 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0129 on 480 degrees of freedom
## Multiple R-squared: 0.5241, Adjusted R-squared: 0.5211
## F-statistic: 176.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047499 -0.006006 0.000174 0.008296 0.037097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.149e+00 1.036e-01 -11.088 <2e-16 ***
## time_value 6.732e-05 5.720e-06 11.769 <2e-16 ***
## RestaurantRestrict -6.404e-01 2.914e-01 -2.198 0.0285 *
## time_value:RestaurantRestrict 3.249e-05 1.583e-05 2.053 0.0406 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01299 on 480 degrees of freedom
## Multiple R-squared: 0.4506, Adjusted R-squared: 0.4472
## F-statistic: 131.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045334 -0.003472 0.000660 0.005934 0.026009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.130e-01 8.931e-02 -5.744 1.64e-08 ***
## time_value 3.180e-05 4.929e-06 6.451 2.72e-10 ***
## RestaurantRestrict -5.873e-01 2.482e-01 -2.367 0.0183 *
## time_value:RestaurantRestrict 3.016e-05 1.348e-05 2.237 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01114 on 480 degrees of freedom
## Multiple R-squared: 0.4669, Adjusted R-squared: 0.4636
## F-statistic: 140.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056987 -0.018275 -0.002854 0.011642 0.059863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.032e-01 1.061e-01 4.742 2.79e-06 ***
## time_value -2.356e-05 5.824e-06 -4.045 6.09e-05 ***
## RestaurantRestrict 2.223e+00 4.088e+00 0.544 0.587
## time_value:RestaurantRestrict -1.226e-04 2.226e-04 -0.551 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02451 on 480 degrees of freedom
## Multiple R-squared: 0.1439, Adjusted R-squared: 0.1386
## F-statistic: 26.9 on 3 and 480 DF, p-value: 4.259e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051911 -0.005789 0.000825 0.008702 0.029125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.277e+00 1.057e-01 -12.082 < 2e-16 ***
## time_value 7.421e-05 5.833e-06 12.723 < 2e-16 ***
## RestaurantRestrict -8.304e-01 3.008e-01 -2.761 0.00598 **
## time_value:RestaurantRestrict 4.273e-05 1.633e-05 2.616 0.00917 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01331 on 480 degrees of freedom
## Multiple R-squared: 0.448, Adjusted R-squared: 0.4446
## F-statistic: 129.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.040605 -0.006791 0.000953 0.007909 0.029045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.822e-01 9.094e-02 -7.502 3.06e-13 ***
## time_value 4.097e-05 5.019e-06 8.163 2.89e-15 ***
## RestaurantRestrict -1.567e+00 2.619e-01 -5.983 4.28e-09 ***
## time_value:RestaurantRestrict 8.370e-05 1.422e-05 5.884 7.50e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0115 on 480 degrees of freedom
## Multiple R-squared: 0.3085, Adjusted R-squared: 0.3041
## F-statistic: 71.37 on 3 and 480 DF, p-value: < 2.2e-16Completely staying at home
Emergency Declaration
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*EmergDec,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109983 -0.019150 -0.001361 0.018133 0.130368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.531e+00 2.396e-01 23.084 <2e-16 ***
## time_value -2.885e-04 1.323e-05 -21.811 <2e-16 ***
## EmergDec 9.270e-01 6.427e-01 1.442 0.15
## time_value:EmergDec -4.582e-05 3.492e-05 -1.312 0.19
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02946 on 480 degrees of freedom
## Multiple R-squared: 0.5622, Adjusted R-squared: 0.5594
## F-statistic: 205.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065946 -0.016417 -0.001261 0.012882 0.158739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.016e+00 2.035e-01 19.73 < 2e-16 ***
## time_value -2.087e-04 1.123e-05 -18.58 < 2e-16 ***
## EmergDec 2.889e+00 5.588e-01 5.17 3.45e-07 ***
## time_value:EmergDec -1.527e-04 3.036e-05 -5.03 6.96e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02526 on 480 degrees of freedom
## Multiple R-squared: 0.5148, Adjusted R-squared: 0.5118
## F-statistic: 169.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083551 -0.014135 -0.001872 0.012127 0.144480
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.150e+00 1.892e-01 21.931 < 2e-16 ***
## time_value -2.156e-04 1.045e-05 -20.636 < 2e-16 ***
## EmergDec 3.021e+00 5.075e-01 5.953 5.09e-09 ***
## time_value:EmergDec -1.600e-04 2.757e-05 -5.803 1.19e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02326 on 480 degrees of freedom
## Multiple R-squared: 0.574, Adjusted R-squared: 0.5713
## F-statistic: 215.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.117385 -0.016340 0.001147 0.015316 0.108483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.846e+00 2.136e-01 22.690 < 2e-16 ***
## time_value -2.506e-04 1.179e-05 -21.253 < 2e-16 ***
## EmergDec 1.822e+00 5.729e-01 3.181 0.00156 **
## time_value:EmergDec -9.287e-05 3.113e-05 -2.984 0.00299 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02626 on 480 degrees of freedom
## Multiple R-squared: 0.5875, Adjusted R-squared: 0.5849
## F-statistic: 227.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20017 -0.01318 -0.00099 0.01279 0.10969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.991e+00 2.724e-01 18.320 < 2e-16 ***
## time_value -2.598e-04 1.504e-05 -17.271 < 2e-16 ***
## EmergDec 2.041e+00 6.900e-01 2.957 0.00326 **
## time_value:EmergDec -1.019e-04 3.751e-05 -2.718 0.00681 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0327 on 480 degrees of freedom
## Multiple R-squared: 0.6643, Adjusted R-squared: 0.6622
## F-statistic: 316.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.133763 -0.019062 -0.004696 0.012677 0.183633
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.006e+00 2.665e-01 18.780 < 2e-16 ***
## time_value -2.597e-04 1.471e-05 -17.649 < 2e-16 ***
## EmergDec 5.371e+00 7.149e-01 7.513 2.85e-13 ***
## time_value:EmergDec -2.851e-04 3.884e-05 -7.340 9.19e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03277 on 480 degrees of freedom
## Multiple R-squared: 0.5518, Adjusted R-squared: 0.549
## F-statistic: 197 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.19659 -0.01937 -0.00454 0.01440 0.16934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.784e+00 2.944e-01 16.25 <2e-16 ***
## time_value -2.486e-04 1.625e-05 -15.29 <2e-16 ***
## EmergDec 7.981e+00 7.806e-01 10.22 <2e-16 ***
## time_value:EmergDec -4.251e-04 4.241e-05 -10.02 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03603 on 480 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6123
## F-statistic: 255.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.191012 -0.016708 -0.001317 0.014675 0.182501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.629e+00 2.463e-01 10.674 <2e-16 ***
## time_value -1.281e-04 1.360e-05 -9.425 <2e-16 ***
## EmergDec 7.348e+00 6.606e-01 11.123 <2e-16 ***
## time_value:EmergDec -3.904e-04 3.589e-05 -10.878 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03028 on 480 degrees of freedom
## Multiple R-squared: 0.7621, Adjusted R-squared: 0.7606
## F-statistic: 512.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080765 -0.018389 -0.003991 0.015722 0.127445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.908e+00 2.250e-01 21.816 <2e-16 ***
## time_value -2.542e-04 1.242e-05 -20.474 <2e-16 ***
## EmergDec 5.768e+00 6.178e-01 9.336 <2e-16 ***
## time_value:EmergDec -3.059e-04 3.356e-05 -9.114 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02792 on 480 degrees of freedom
## Multiple R-squared: 0.6603, Adjusted R-squared: 0.6581
## F-statistic: 310.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.152473 -0.015204 0.001074 0.014522 0.137765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.676e+00 2.435e-01 15.098 < 2e-16 ***
## time_value -1.876e-04 1.344e-05 -13.958 < 2e-16 ***
## EmergDec 4.352e+00 6.381e-01 6.820 2.75e-11 ***
## time_value:EmergDec -2.305e-04 3.468e-05 -6.648 8.11e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02965 on 480 degrees of freedom
## Multiple R-squared: 0.5198, Adjusted R-squared: 0.5168
## F-statistic: 173.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061911 -0.015748 -0.001066 0.012109 0.138911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.012e+00 1.951e-01 20.564 <2e-16 ***
## time_value -2.072e-04 1.077e-05 -19.246 <2e-16 ***
## EmergDec 4.918e+00 5.357e-01 9.181 <2e-16 ***
## time_value:EmergDec -2.613e-04 2.910e-05 -8.980 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02421 on 480 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6211
## F-statistic: 265 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.157989 -0.015909 0.000545 0.014891 0.121430
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.725e+00 2.579e-01 18.324 < 2e-16 ***
## time_value -2.465e-04 1.424e-05 -17.317 < 2e-16 ***
## EmergDec -1.969e+00 6.531e-01 -3.015 0.00271 **
## time_value:EmergDec 1.144e-04 3.550e-05 3.223 0.00136 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03095 on 480 degrees of freedom
## Multiple R-squared: 0.5989, Adjusted R-squared: 0.5964
## F-statistic: 238.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.150164 -0.018128 -0.003614 0.013441 0.167319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.638e+00 2.662e-01 21.180 < 2e-16 ***
## time_value -2.967e-04 1.470e-05 -20.188 < 2e-16 ***
## EmergDec 3.501e+00 6.977e-01 5.018 7.36e-07 ***
## time_value:EmergDec -1.846e-04 3.791e-05 -4.868 1.53e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03242 on 480 degrees of freedom
## Multiple R-squared: 0.5556, Adjusted R-squared: 0.5528
## F-statistic: 200 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063553 -0.015316 -0.002543 0.013936 0.139276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.379e+00 1.920e-01 28.008 < 2e-16 ***
## time_value -2.806e-04 1.060e-05 -26.469 < 2e-16 ***
## EmergDec 1.839e+00 5.274e-01 3.487 0.000534 ***
## time_value:EmergDec -9.508e-05 2.865e-05 -3.319 0.000973 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02384 on 480 degrees of freedom
## Multiple R-squared: 0.6578, Adjusted R-squared: 0.6557
## F-statistic: 307.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.177270 -0.020200 -0.003146 0.016341 0.211408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.063e+00 2.744e-01 18.453 < 2e-16 ***
## time_value -2.640e-04 1.515e-05 -17.430 < 2e-16 ***
## EmergDec 5.296e+00 7.192e-01 7.364 7.82e-13 ***
## time_value:EmergDec -2.800e-04 3.908e-05 -7.163 2.98e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03342 on 480 degrees of freedom
## Multiple R-squared: 0.5925, Adjusted R-squared: 0.5899
## F-statistic: 232.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.131786 -0.018172 -0.003659 0.014063 0.164096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.789e+00 2.432e-01 19.695 < 2e-16 ***
## time_value -2.490e-04 1.343e-05 -18.550 < 2e-16 ***
## EmergDec 4.537e+00 6.301e-01 7.200 2.34e-12 ***
## time_value:EmergDec -2.413e-04 3.425e-05 -7.046 6.42e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02947 on 480 degrees of freedom
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.547
## F-statistic: 195.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.125307 -0.018318 -0.003701 0.013057 0.129586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.162e+00 2.293e-01 22.515 < 2e-16 ***
## time_value -2.700e-04 1.266e-05 -21.332 < 2e-16 ***
## EmergDec 4.734e+00 6.222e-01 7.609 1.48e-13 ***
## time_value:EmergDec -2.515e-04 3.380e-05 -7.439 4.71e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02832 on 480 degrees of freedom
## Multiple R-squared: 0.6041, Adjusted R-squared: 0.6016
## F-statistic: 244.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.125031 -0.017754 -0.001933 0.013987 0.131631
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.720e+00 2.285e-01 25.034 <2e-16 ***
## time_value -3.005e-04 1.262e-05 -23.819 <2e-16 ***
## EmergDec 8.608e-01 5.921e-01 1.454 0.147
## time_value:EmergDec -4.156e-05 3.218e-05 -1.291 0.197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02769 on 480 degrees of freedom
## Multiple R-squared: 0.5965, Adjusted R-squared: 0.594
## F-statistic: 236.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.129275 -0.015917 -0.000727 0.013504 0.113919
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.116e+00 2.016e-01 20.417 < 2e-16 ***
## time_value -2.131e-04 1.113e-05 -19.147 < 2e-16 ***
## EmergDec 4.057e+00 5.407e-01 7.503 3.05e-13 ***
## time_value:EmergDec -2.157e-04 2.938e-05 -7.341 9.12e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02479 on 480 degrees of freedom
## Multiple R-squared: 0.5635, Adjusted R-squared: 0.5608
## F-statistic: 206.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.218825 -0.019322 -0.004031 0.014017 0.127703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.759e+00 2.873e-01 16.564 <2e-16 ***
## time_value -2.468e-04 1.586e-05 -15.560 <2e-16 ***
## EmergDec 6.877e+00 7.617e-01 9.029 <2e-16 ***
## time_value:EmergDec -3.645e-04 4.139e-05 -8.806 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03516 on 480 degrees of freedom
## Multiple R-squared: 0.6562, Adjusted R-squared: 0.654
## F-statistic: 305.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.190095 -0.017791 -0.003592 0.012935 0.226787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.611e+00 3.103e-01 14.863 < 2e-16 ***
## time_value -2.384e-04 1.713e-05 -13.918 < 2e-16 ***
## EmergDec 3.563e+00 7.948e-01 4.483 9.23e-06 ***
## time_value:EmergDec -1.850e-04 4.320e-05 -4.282 2.24e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03742 on 480 degrees of freedom
## Multiple R-squared: 0.5902, Adjusted R-squared: 0.5876
## F-statistic: 230.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071682 -0.020593 -0.002905 0.014712 0.162470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.504e+00 2.493e-01 22.077 < 2e-16 ***
## time_value -2.866e-04 1.376e-05 -20.829 < 2e-16 ***
## EmergDec 3.431e+00 6.846e-01 5.011 7.61e-07 ***
## time_value:EmergDec -1.813e-04 3.719e-05 -4.875 1.48e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03094 on 480 degrees of freedom
## Multiple R-squared: 0.569, Adjusted R-squared: 0.5663
## F-statistic: 211.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.191430 -0.022231 -0.005835 0.018351 0.162026
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.457e+00 2.331e-01 14.83 <2e-16 ***
## time_value -1.742e-04 1.285e-05 -13.56 <2e-16 ***
## EmergDec 1.140e+01 1.016e+00 11.23 <2e-16 ***
## time_value:EmergDec -6.144e-04 5.514e-05 -11.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03941 on 480 degrees of freedom
## Multiple R-squared: 0.4826, Adjusted R-squared: 0.4794
## F-statistic: 149.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.092157 -0.021766 -0.004306 0.015423 0.152490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.083e+00 2.597e-01 19.577 < 2e-16 ***
## time_value -2.648e-04 1.433e-05 -18.476 < 2e-16 ***
## EmergDec 5.460e+00 7.131e-01 7.657 1.06e-13 ***
## time_value:EmergDec -2.894e-04 3.873e-05 -7.472 3.77e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03223 on 480 degrees of freedom
## Multiple R-squared: 0.5822, Adjusted R-squared: 0.5796
## F-statistic: 222.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073300 -0.017873 -0.004603 0.011477 0.169086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.869e+00 2.229e-01 21.842 < 2e-16 ***
## time_value -2.537e-04 1.231e-05 -20.618 < 2e-16 ***
## EmergDec 4.520e+00 6.122e-01 7.382 6.91e-13 ***
## time_value:EmergDec -2.400e-04 3.326e-05 -7.217 2.10e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02767 on 480 degrees of freedom
## Multiple R-squared: 0.5874, Adjusted R-squared: 0.5849
## F-statistic: 227.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048130 -0.014453 -0.002464 0.012036 0.149518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.589e+00 1.872e-01 19.169 < 2e-16 ***
## time_value -1.852e-04 1.034e-05 -17.918 < 2e-16 ***
## EmergDec 3.399e+00 5.142e-01 6.610 1.02e-10 ***
## time_value:EmergDec -1.809e-04 2.793e-05 -6.477 2.32e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02324 on 480 degrees of freedom
## Multiple R-squared: 0.5207, Adjusted R-squared: 0.5177
## F-statistic: 173.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.091714 -0.017929 -0.002068 0.014364 0.119791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.921e+00 2.144e-01 22.958 <2e-16 ***
## time_value -2.553e-04 1.183e-05 -21.572 <2e-16 ***
## EmergDec 1.240e+00 5.818e-01 2.132 0.0336 *
## time_value:EmergDec -6.329e-05 3.161e-05 -2.002 0.0458 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02648 on 480 degrees of freedom
## Multiple R-squared: 0.562, Adjusted R-squared: 0.5593
## F-statistic: 205.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12536 -0.01351 -0.00103 0.01411 0.11827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.878e+00 2.108e-01 18.399 < 2e-16 ***
## time_value -1.992e-04 1.164e-05 -17.120 < 2e-16 ***
## EmergDec 2.931e+00 5.589e-01 5.244 2.36e-07 ***
## time_value:EmergDec -1.542e-04 3.037e-05 -5.079 5.45e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0258 on 480 degrees of freedom
## Multiple R-squared: 0.5113, Adjusted R-squared: 0.5083
## F-statistic: 167.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074236 -0.022499 -0.004127 0.017198 0.133289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.122e+00 2.633e-01 19.452 < 2e-16 ***
## time_value -2.672e-04 1.454e-05 -18.379 < 2e-16 ***
## EmergDec 3.355e+00 7.232e-01 4.639 4.51e-06 ***
## time_value:EmergDec -1.777e-04 3.928e-05 -4.523 7.71e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03269 on 480 degrees of freedom
## Multiple R-squared: 0.5138, Adjusted R-squared: 0.5108
## F-statistic: 169.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072325 -0.018380 -0.003968 0.012784 0.134941
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.844e+00 2.340e-01 24.98 < 2e-16 ***
## time_value -3.075e-04 1.291e-05 -23.81 < 2e-16 ***
## EmergDec 5.301e+00 6.425e-01 8.25 1.54e-15 ***
## time_value:EmergDec -2.820e-04 3.490e-05 -8.08 5.29e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02904 on 480 degrees of freedom
## Multiple R-squared: 0.6536, Adjusted R-squared: 0.6514
## F-statistic: 301.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.091978 -0.020330 -0.004499 0.013329 0.187174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.923e+00 2.597e-01 18.953 <2e-16 ***
## time_value -2.559e-04 1.434e-05 -17.846 <2e-16 ***
## EmergDec 6.212e+00 7.133e-01 8.709 <2e-16 ***
## time_value:EmergDec -3.304e-04 3.875e-05 -8.528 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03224 on 480 degrees of freedom
## Multiple R-squared: 0.5802, Adjusted R-squared: 0.5776
## F-statistic: 221.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23908 -0.02094 -0.00421 0.01495 0.16604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.101e+00 3.341e-01 15.27 <2e-16 ***
## time_value -2.666e-04 1.844e-05 -14.46 <2e-16 ***
## EmergDec 9.820e+00 8.756e-01 11.21 <2e-16 ***
## time_value:EmergDec -5.227e-04 4.759e-05 -10.98 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04069 on 480 degrees of freedom
## Multiple R-squared: 0.6827, Adjusted R-squared: 0.6807
## F-statistic: 344.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.096703 -0.015661 -0.001844 0.012977 0.138874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.192e+00 2.112e-01 24.587 <2e-16 ***
## time_value -2.699e-04 1.166e-05 -23.147 <2e-16 ***
## EmergDec -3.706e-01 5.665e-01 -0.654 0.513
## time_value:EmergDec 2.586e-05 3.078e-05 0.840 0.401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02597 on 480 degrees of freedom
## Multiple R-squared: 0.5741, Adjusted R-squared: 0.5715
## F-statistic: 215.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.155203 -0.014986 -0.001399 0.013124 0.125810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.499e+00 2.029e-01 27.103 < 2e-16 ***
## time_value -2.865e-04 1.120e-05 -25.581 < 2e-16 ***
## EmergDec 3.510e+00 5.507e-01 6.373 4.34e-10 ***
## time_value:EmergDec -1.832e-04 2.992e-05 -6.122 1.92e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02507 on 480 degrees of freedom
## Multiple R-squared: 0.7019, Adjusted R-squared: 0.7
## F-statistic: 376.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.222752 -0.019252 -0.002912 0.015076 0.152834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.149e+00 3.117e-01 16.520 <2e-16 ***
## time_value -2.677e-04 1.721e-05 -15.556 <2e-16 ***
## EmergDec 7.349e+00 8.077e-01 9.099 <2e-16 ***
## time_value:EmergDec -3.899e-04 4.390e-05 -8.882 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03777 on 480 degrees of freedom
## Multiple R-squared: 0.6352, Adjusted R-squared: 0.633
## F-statistic: 278.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13936 -0.01917 -0.00358 0.01542 0.11517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.223e+00 2.474e-01 21.110 < 2e-16 ***
## time_value -2.723e-04 1.366e-05 -19.934 < 2e-16 ***
## EmergDec 5.056e+00 6.485e-01 7.797 3.98e-14 ***
## time_value:EmergDec -2.687e-04 3.524e-05 -7.625 1.32e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03013 on 480 degrees of freedom
## Multiple R-squared: 0.5856, Adjusted R-squared: 0.583
## F-statistic: 226.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.064312 -0.016271 -0.003666 0.009762 0.170381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.495e+00 2.177e-01 20.648 < 2e-16 ***
## time_value -2.344e-04 1.202e-05 -19.504 < 2e-16 ***
## EmergDec 4.115e+00 5.979e-01 6.883 1.83e-11 ***
## time_value:EmergDec -2.187e-04 3.248e-05 -6.735 4.70e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02702 on 480 degrees of freedom
## Multiple R-squared: 0.558, Adjusted R-squared: 0.5552
## F-statistic: 202 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.136837 -0.014834 -0.001047 0.013255 0.127053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.148e+00 2.316e-01 22.232 <2e-16 ***
## time_value -2.672e-04 1.278e-05 -20.903 <2e-16 ***
## EmergDec 9.142e-01 6.000e-01 1.524 0.128
## time_value:EmergDec -4.331e-05 3.261e-05 -1.328 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02806 on 480 degrees of freedom
## Multiple R-squared: 0.5673, Adjusted R-squared: 0.5646
## F-statistic: 209.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.179078 -0.018279 -0.003477 0.015768 0.121425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.505e+00 2.823e-01 19.497 < 2e-16 ***
## time_value -2.871e-04 1.559e-05 -18.419 < 2e-16 ***
## EmergDec 4.934e+00 7.316e-01 6.745 4.42e-11 ***
## time_value:EmergDec -2.607e-04 3.976e-05 -6.557 1.42e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03422 on 480 degrees of freedom
## Multiple R-squared: 0.5679, Adjusted R-squared: 0.5652
## F-statistic: 210.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.175031 -0.019393 -0.004216 0.014376 0.139553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.591e+00 2.868e-01 19.496 < 2e-16 ***
## time_value -2.922e-04 1.583e-05 -18.457 < 2e-16 ***
## EmergDec 4.758e+00 7.516e-01 6.330 5.63e-10 ***
## time_value:EmergDec -2.506e-04 4.085e-05 -6.136 1.77e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03492 on 480 degrees of freedom
## Multiple R-squared: 0.5768, Adjusted R-squared: 0.5742
## F-statistic: 218.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05171 -0.01388 -0.00072 0.01268 0.12946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.997e+00 1.850e-01 21.601 < 2e-16 ***
## time_value -2.064e-04 1.021e-05 -20.211 < 2e-16 ***
## EmergDec 2.915e+00 5.081e-01 5.736 1.72e-08 ***
## time_value:EmergDec -1.539e-04 2.760e-05 -5.574 4.15e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02297 on 480 degrees of freedom
## Multiple R-squared: 0.5601, Adjusted R-squared: 0.5573
## F-statistic: 203.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.065965 -0.020634 -0.003667 0.013203 0.213559
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.282e+00 2.578e-01 20.487 < 2e-16 ***
## time_value -2.760e-04 1.423e-05 -19.394 < 2e-16 ***
## EmergDec 3.303e+00 7.081e-01 4.665 4.00e-06 ***
## time_value:EmergDec -1.750e-04 3.846e-05 -4.551 6.79e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.032 on 480 degrees of freedom
## Multiple R-squared: 0.547, Adjusted R-squared: 0.5442
## F-statistic: 193.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.104761 -0.016023 -0.001609 0.013473 0.118978
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.205e+00 1.990e-01 21.132 < 2e-16 ***
## time_value -2.176e-04 1.098e-05 -19.808 < 2e-16 ***
## EmergDec 3.793e+00 5.400e-01 7.023 7.45e-12 ***
## time_value:EmergDec -2.013e-04 2.934e-05 -6.862 2.10e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02458 on 480 degrees of freedom
## Multiple R-squared: 0.5685, Adjusted R-squared: 0.5658
## F-statistic: 210.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077275 -0.015051 -0.001002 0.011666 0.108475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.434e+00 1.965e-01 22.570 <2e-16 ***
## time_value -2.301e-04 1.084e-05 -21.221 <2e-16 ***
## EmergDec 5.009e+00 5.395e-01 9.285 <2e-16 ***
## time_value:EmergDec -2.656e-04 2.931e-05 -9.062 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02438 on 480 degrees of freedom
## Multiple R-squared: 0.6647, Adjusted R-squared: 0.6626
## F-statistic: 317.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.139724 -0.014345 -0.001546 0.011959 0.123060
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.1365384 0.2300631 22.327 < 2e-16 ***
## time_value -0.0002683 0.0000127 -21.125 < 2e-16 ***
## EmergDec 2.0709470 0.5961594 3.474 0.00056 ***
## time_value:EmergDec -0.0001067 0.0000324 -3.292 0.00107 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02788 on 480 degrees of freedom
## Multiple R-squared: 0.5661, Adjusted R-squared: 0.5634
## F-statistic: 208.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.155581 -0.016645 -0.003953 0.014385 0.137872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.662e+00 2.176e-01 21.422 < 2e-16 ***
## time_value -2.419e-04 1.201e-05 -20.137 < 2e-16 ***
## EmergDec 4.324e+00 5.906e-01 7.322 1.04e-12 ***
## time_value:EmergDec -2.274e-04 3.209e-05 -7.086 4.95e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02689 on 480 degrees of freedom
## Multiple R-squared: 0.6721, Adjusted R-squared: 0.6701
## F-statistic: 328 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.122704 -0.020767 -0.003603 0.018457 0.115512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.663e+00 2.612e-01 25.511 <2e-16 ***
## time_value -3.501e-04 1.442e-05 -24.288 <2e-16 ***
## EmergDec 1.823e+00 7.172e-01 2.542 0.0113 *
## time_value:EmergDec -9.286e-05 3.896e-05 -2.383 0.0175 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03242 on 480 degrees of freedom
## Multiple R-squared: 0.6092, Adjusted R-squared: 0.6068
## F-statistic: 249.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14440 -0.01810 -0.00308 0.01507 0.17359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.717e+00 2.986e-01 15.797 <2e-16 ***
## time_value -2.439e-04 1.649e-05 -14.795 <2e-16 ***
## EmergDec 5.353e-01 7.314e-01 0.732 0.465
## time_value:EmergDec -2.208e-05 3.977e-05 -0.555 0.579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03532 on 480 degrees of freedom
## Multiple R-squared: 0.4887, Adjusted R-squared: 0.4855
## F-statistic: 152.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.159477 -0.019740 -0.005229 0.016935 0.170593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.943e+00 2.616e-01 22.714 < 2e-16 ***
## time_value -3.117e-04 1.444e-05 -21.582 < 2e-16 ***
## EmergDec 3.569e+00 7.101e-01 5.026 7.09e-07 ***
## time_value:EmergDec -1.872e-04 3.858e-05 -4.852 1.66e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03232 on 480 degrees of freedom
## Multiple R-squared: 0.5823, Adjusted R-squared: 0.5796
## F-statistic: 223 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066301 -0.018177 -0.003843 0.015190 0.119501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.343e+00 2.143e-01 29.597 < 2e-16 ***
## time_value -3.337e-04 1.183e-05 -28.210 < 2e-16 ***
## EmergDec 1.760e+00 5.955e-01 2.956 0.00327 **
## time_value:EmergDec -9.028e-05 3.235e-05 -2.791 0.00546 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02673 on 480 degrees of freedom
## Multiple R-squared: 0.686, Adjusted R-squared: 0.684
## F-statistic: 349.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * EmergDec, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061919 -0.016963 -0.003811 0.012710 0.126162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.162e+00 2.157e-01 23.935 < 2e-16 ***
## time_value -2.683e-04 1.190e-05 -22.534 < 2e-16 ***
## EmergDec 3.140e+00 5.923e-01 5.303 1.75e-07 ***
## time_value:EmergDec -1.665e-04 3.217e-05 -5.175 3.35e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02677 on 480 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.6233
## F-statistic: 267.4 on 3 and 480 DF, p-value: < 2.2e-16School Closure with full time work prop
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*SchoolClose,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.100490 -0.021962 0.000848 0.020787 0.107678
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.226e+00 1.527e-01 21.124 < 2e-16 ***
## time_value -1.610e-04 8.392e-06 -19.179 < 2e-16 ***
## SchoolClose 7.576e+00 1.581e+00 4.792 2.21e-06 ***
## time_value:SchoolClose -4.093e-04 8.592e-05 -4.763 2.53e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0325 on 480 degrees of freedom
## Multiple R-squared: 0.4674, Adjusted R-squared: 0.464
## F-statistic: 140.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069906 -0.016793 -0.002113 0.017259 0.131536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.003e+00 1.270e-01 15.772 < 2e-16 ***
## time_value -9.726e-05 6.973e-06 -13.947 < 2e-16 ***
## SchoolClose 1.311e+01 2.064e+00 6.353 4.92e-10 ***
## time_value:SchoolClose -7.106e-04 1.123e-04 -6.330 5.64e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02824 on 480 degrees of freedom
## Multiple R-squared: 0.3935, Adjusted R-squared: 0.3898
## F-statistic: 103.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060327 -0.016594 -0.000931 0.014979 0.123958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.607e+00 1.243e-01 20.974 < 2e-16 ***
## time_value -1.302e-04 6.837e-06 -19.043 < 2e-16 ***
## SchoolClose 7.444e+00 9.274e-01 8.027 7.76e-15 ***
## time_value:SchoolClose -4.017e-04 5.037e-05 -7.976 1.12e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02487 on 480 degrees of freedom
## Multiple R-squared: 0.5131, Adjusted R-squared: 0.51
## F-statistic: 168.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081827 -0.018620 -0.001945 0.019999 0.104505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.148e+00 1.567e-01 13.707 < 2e-16 ***
## time_value -1.013e-04 8.620e-06 -11.757 < 2e-16 ***
## SchoolClose 4.393e+00 1.253e+00 3.504 0.000500 ***
## time_value:SchoolClose -2.350e-04 6.809e-05 -3.452 0.000606 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03191 on 480 degrees of freedom
## Multiple R-squared: 0.3908, Adjusted R-squared: 0.387
## F-statistic: 102.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083257 -0.013889 -0.001469 0.011609 0.109630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.987e+00 1.916e-01 26.02 <2e-16 ***
## time_value -2.595e-04 1.058e-05 -24.54 <2e-16 ***
## SchoolClose 5.482e+00 5.263e-01 10.42 <2e-16 ***
## time_value:SchoolClose -2.880e-04 2.859e-05 -10.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02379 on 480 degrees of freedom
## Multiple R-squared: 0.8224, Adjusted R-squared: 0.8213
## F-statistic: 740.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.092222 -0.019733 -0.003921 0.019706 0.193414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.202e+00 1.637e-01 13.450 <2e-16 ***
## time_value -1.046e-04 8.997e-06 -11.622 <2e-16 ***
## SchoolClose 2.023e+01 2.027e+00 9.978 <2e-16 ***
## time_value:SchoolClose -1.095e-03 1.102e-04 -9.939 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03538 on 480 degrees of freedom
## Multiple R-squared: 0.4778, Adjusted R-squared: 0.4745
## F-statistic: 146.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.105911 -0.022655 -0.000983 0.022179 0.181914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.687e+00 1.743e-01 9.684 < 2e-16 ***
## time_value -7.729e-05 9.578e-06 -8.070 5.71e-15 ***
## SchoolClose 2.004e+01 1.833e+00 10.929 < 2e-16 ***
## time_value:SchoolClose -1.083e-03 9.963e-05 -10.867 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03711 on 480 degrees of freedom
## Multiple R-squared: 0.5912, Adjusted R-squared: 0.5886
## F-statistic: 231.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08397 -0.01649 -0.00174 0.01388 0.18295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.545e+00 2.220e-01 11.46 <2e-16 ***
## time_value -1.235e-04 1.225e-05 -10.08 <2e-16 ***
## SchoolClose 8.738e+00 6.168e-01 14.17 <2e-16 ***
## time_value:SchoolClose -4.657e-04 3.350e-05 -13.90 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02768 on 480 degrees of freedom
## Multiple R-squared: 0.8011, Adjusted R-squared: 0.7999
## F-statistic: 644.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080765 -0.018389 -0.003991 0.015722 0.127445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.908e+00 2.250e-01 21.816 <2e-16 ***
## time_value -2.542e-04 1.242e-05 -20.474 <2e-16 ***
## SchoolClose 5.768e+00 6.178e-01 9.336 <2e-16 ***
## time_value:SchoolClose -3.059e-04 3.356e-05 -9.114 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02792 on 480 degrees of freedom
## Multiple R-squared: 0.6603, Adjusted R-squared: 0.6581
## F-statistic: 310.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.093449 -0.016235 -0.000408 0.018551 0.129500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.386e+00 1.469e-01 9.435 < 2e-16 ***
## time_value -6.091e-05 8.073e-06 -7.545 2.29e-13 ***
## SchoolClose 1.102e+01 1.439e+00 7.658 1.05e-13 ***
## time_value:SchoolClose -5.947e-04 7.819e-05 -7.605 1.51e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03095 on 480 degrees of freedom
## Multiple R-squared: 0.4767, Adjusted R-squared: 0.4734
## F-statistic: 145.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073844 -0.017740 -0.000351 0.017973 0.117679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.557e+00 1.335e-01 11.661 < 2e-16 ***
## time_value -7.145e-05 7.337e-06 -9.738 < 2e-16 ***
## SchoolClose 1.394e+01 1.659e+00 8.400 5.08e-16 ***
## time_value:SchoolClose -7.539e-04 9.019e-05 -8.359 6.87e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02899 on 480 degrees of freedom
## Multiple R-squared: 0.4604, Adjusted R-squared: 0.457
## F-statistic: 136.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095778 -0.021291 -0.002685 0.023481 0.154760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.394e+00 1.967e-01 7.087 4.91e-12 ***
## time_value -6.232e-05 1.083e-05 -5.753 1.56e-08 ***
## SchoolClose 4.749e+00 1.080e+00 4.399 1.34e-05 ***
## time_value:SchoolClose -2.531e-04 5.861e-05 -4.319 1.90e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0361 on 480 degrees of freedom
## Multiple R-squared: 0.4543, Adjusted R-squared: 0.4509
## F-statistic: 133.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070897 -0.019811 -0.004492 0.014533 0.167243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.650e+00 2.379e-01 23.749 < 2e-16 ***
## time_value -2.973e-04 1.313e-05 -22.643 < 2e-16 ***
## SchoolClose 5.218e+00 6.611e-01 7.892 2.03e-14 ***
## time_value:SchoolClose -2.774e-04 3.591e-05 -7.724 6.60e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02967 on 480 degrees of freedom
## Multiple R-squared: 0.6276, Adjusted R-squared: 0.6253
## F-statistic: 269.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070391 -0.017890 -0.002467 0.018572 0.115194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.966e+00 1.273e-01 23.291 < 2e-16 ***
## time_value -1.471e-04 6.994e-06 -21.028 < 2e-16 ***
## SchoolClose 1.151e+01 1.815e+00 6.344 5.19e-10 ***
## time_value:SchoolClose -6.231e-04 9.866e-05 -6.316 6.13e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02791 on 480 degrees of freedom
## Multiple R-squared: 0.5308, Adjusted R-squared: 0.5279
## F-statistic: 181 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.101365 -0.024021 0.000909 0.021389 0.239791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.610e+00 1.592e-01 10.112 < 2e-16 ***
## time_value -7.298e-05 8.744e-06 -8.346 7.56e-16 ***
## SchoolClose 1.979e+01 2.536e+00 7.803 3.80e-14 ***
## time_value:SchoolClose -1.070e-03 1.379e-04 -7.761 5.11e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03539 on 480 degrees of freedom
## Multiple R-squared: 0.5429, Adjusted R-squared: 0.54
## F-statistic: 190 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.078092 -0.017839 -0.002216 0.018040 0.181451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.657e+00 1.291e-01 20.576 <2e-16 ***
## time_value -1.311e-04 7.093e-06 -18.479 <2e-16 ***
## SchoolClose 1.961e+01 2.006e+00 9.776 <2e-16 ***
## time_value:SchoolClose -1.062e-03 1.091e-04 -9.739 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02859 on 480 degrees of freedom
## Multiple R-squared: 0.5764, Adjusted R-squared: 0.5737
## F-statistic: 217.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.086800 -0.021886 -0.000699 0.020683 0.128381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.534e+00 1.393e-01 18.197 <2e-16 ***
## time_value -1.246e-04 7.644e-06 -16.296 <2e-16 ***
## SchoolClose 3.729e+00 3.980e+00 0.937 0.349
## time_value:SchoolClose -1.982e-04 2.166e-04 -0.915 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0319 on 480 degrees of freedom
## Multiple R-squared: 0.4979, Adjusted R-squared: 0.4948
## F-statistic: 158.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047735 -0.018851 -0.002091 0.014601 0.131351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.702e+00 2.023e-01 28.179 < 2e-16 ***
## time_value -2.995e-04 1.117e-05 -26.813 < 2e-16 ***
## SchoolClose 2.526e+00 5.622e-01 4.492 8.83e-06 ***
## time_value:SchoolClose -1.316e-04 3.054e-05 -4.310 1.98e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02524 on 480 degrees of freedom
## Multiple R-squared: 0.665, Adjusted R-squared: 0.6629
## F-statistic: 317.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.146629 -0.017150 -0.001934 0.019835 0.104996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.051e+00 1.268e-01 16.179 < 2e-16 ***
## time_value -9.884e-05 6.963e-06 -14.194 < 2e-16 ***
## SchoolClose 1.174e+01 1.979e+00 5.933 5.69e-09 ***
## time_value:SchoolClose -6.356e-04 1.076e-04 -5.904 6.71e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0282 on 480 degrees of freedom
## Multiple R-squared: 0.4351, Adjusted R-squared: 0.4315
## F-statistic: 123.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.101342 -0.021738 -0.001527 0.022899 0.136696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.259e+00 1.863e-01 6.756 4.12e-11 ***
## time_value -5.315e-05 1.025e-05 -5.188 3.14e-07 ***
## SchoolClose 1.985e+01 1.511e+00 13.137 < 2e-16 ***
## time_value:SchoolClose -1.072e-03 8.206e-05 -13.065 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03796 on 480 degrees of freedom
## Multiple R-squared: 0.5991, Adjusted R-squared: 0.5966
## F-statistic: 239.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.122919 -0.023733 -0.001399 0.024885 0.255019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.568e-01 1.887e-01 3.481 0.000545 ***
## time_value -1.964e-05 1.037e-05 -1.894 0.058777 .
## SchoolClose 1.648e+01 2.062e+00 7.991 1.00e-14 ***
## time_value:SchoolClose -8.900e-04 1.121e-04 -7.940 1.44e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04045 on 480 degrees of freedom
## Multiple R-squared: 0.5212, Adjusted R-squared: 0.5182
## F-statistic: 174.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.087334 -0.019028 -0.000241 0.017541 0.142500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.275e+00 1.568e-01 20.893 < 2e-16 ***
## time_value -1.633e-04 8.615e-06 -18.959 < 2e-16 ***
## SchoolClose 1.493e+01 1.918e+00 7.784 4.34e-14 ***
## time_value:SchoolClose -8.080e-04 1.042e-04 -7.755 5.33e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03349 on 480 degrees of freedom
## Multiple R-squared: 0.4953, Adjusted R-squared: 0.4921
## F-statistic: 157 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.130715 -0.021271 -0.002327 0.021389 0.168582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.639e+00 1.640e-01 16.092 <2e-16 ***
## time_value -1.289e-04 9.006e-06 -14.315 <2e-16 ***
## SchoolClose 2.332e+01 2.451e+00 9.514 <2e-16 ***
## time_value:SchoolClose -1.263e-03 1.333e-04 -9.472 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03631 on 480 degrees of freedom
## Multiple R-squared: 0.5609, Adjusted R-squared: 0.5581
## F-statistic: 204.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.084889 -0.022707 -0.001432 0.019400 0.169550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.156e+00 1.723e-01 12.509 <2e-16 ***
## time_value -1.029e-04 9.473e-06 -10.859 <2e-16 ***
## SchoolClose 1.571e+01 1.746e+00 8.998 <2e-16 ***
## time_value:SchoolClose -8.490e-04 9.485e-05 -8.951 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03645 on 480 degrees of freedom
## Multiple R-squared: 0.4657, Adjusted R-squared: 0.4624
## F-statistic: 139.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077311 -0.020227 -0.001004 0.016704 0.146333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.488e+00 1.389e-01 17.91 <2e-16 ***
## time_value -1.219e-04 7.631e-06 -15.98 <2e-16 ***
## SchoolClose 1.900e+01 1.861e+00 10.21 <2e-16 ***
## time_value:SchoolClose -1.029e-03 1.012e-04 -10.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03027 on 480 degrees of freedom
## Multiple R-squared: 0.5064, Adjusted R-squared: 0.5033
## F-statistic: 164.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063024 -0.015820 -0.000315 0.016405 0.126376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.898e+00 1.104e-01 17.195 < 2e-16 ***
## time_value -9.158e-05 6.057e-06 -15.119 < 2e-16 ***
## SchoolClose 1.733e+01 2.968e+00 5.841 9.59e-09 ***
## time_value:SchoolClose -9.405e-04 1.615e-04 -5.823 1.06e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02519 on 480 degrees of freedom
## Multiple R-squared: 0.437, Adjusted R-squared: 0.4335
## F-statistic: 124.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.078154 -0.019479 0.000879 0.018525 0.116579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.606e+00 1.293e-01 20.145 <2e-16 ***
## time_value -1.271e-04 7.097e-06 -17.911 <2e-16 ***
## SchoolClose -6.687e+00 5.601e+00 -1.194 0.233
## time_value:SchoolClose 3.672e-04 3.050e-04 1.204 0.229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02995 on 480 degrees of freedom
## Multiple R-squared: 0.4397, Adjusted R-squared: 0.4362
## F-statistic: 125.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057077 -0.013869 -0.001258 0.014221 0.118169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.872e+00 1.917e-01 20.196 < 2e-16 ***
## time_value -1.989e-04 1.058e-05 -18.792 < 2e-16 ***
## SchoolClose 4.092e+00 5.327e-01 7.681 8.95e-14 ***
## time_value:SchoolClose -2.170e-04 2.894e-05 -7.500 3.11e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02391 on 480 degrees of freedom
## Multiple R-squared: 0.5802, Adjusted R-squared: 0.5775
## F-statistic: 221.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.091125 -0.020183 0.000309 0.019887 0.140174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.155e+00 1.610e-01 19.601 < 2e-16 ***
## time_value -1.583e-04 8.844e-06 -17.900 < 2e-16 ***
## SchoolClose 9.312e+00 1.931e+00 4.821 1.92e-06 ***
## time_value:SchoolClose -5.033e-04 1.050e-04 -4.794 2.18e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0349 on 480 degrees of freedom
## Multiple R-squared: 0.4459, Adjusted R-squared: 0.4424
## F-statistic: 128.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07874 -0.02134 -0.00188 0.01862 0.13306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.424e+00 1.529e-01 22.388 < 2e-16 ***
## time_value -1.735e-04 8.403e-06 -20.650 < 2e-16 ***
## SchoolClose 1.652e+01 2.158e+00 7.654 1.08e-13 ***
## time_value:SchoolClose -8.937e-04 1.173e-04 -7.621 1.35e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0332 on 480 degrees of freedom
## Multiple R-squared: 0.5472, Adjusted R-squared: 0.5443
## F-statistic: 193.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10975 -0.02061 0.00024 0.02203 0.16260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.088e+00 1.687e-01 12.378 < 2e-16 ***
## time_value -9.904e-05 9.269e-06 -10.684 < 2e-16 ***
## SchoolClose 1.399e+01 2.146e+00 6.517 1.82e-10 ***
## time_value:SchoolClose -7.557e-04 1.167e-04 -6.476 2.33e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03681 on 480 degrees of freedom
## Multiple R-squared: 0.4528, Adjusted R-squared: 0.4494
## F-statistic: 132.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.124512 -0.023174 0.000044 0.023469 0.173737
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.189e+00 1.964e-01 6.055 2.83e-09 ***
## time_value -5.020e-05 1.080e-05 -4.647 4.35e-06 ***
## SchoolClose 2.799e+01 1.671e+00 16.746 < 2e-16 ***
## time_value:SchoolClose -1.513e-03 9.079e-05 -16.664 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04033 on 480 degrees of freedom
## Multiple R-squared: 0.6882, Adjusted R-squared: 0.6863
## F-statistic: 353.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057974 -0.015953 -0.001967 0.012211 0.139425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.124e+00 2.033e-01 25.210 <2e-16 ***
## time_value -2.661e-04 1.122e-05 -23.715 <2e-16 ***
## SchoolClose 3.285e-01 5.648e-01 0.582 0.561
## time_value:SchoolClose -1.203e-05 3.068e-05 -0.392 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02535 on 480 degrees of freedom
## Multiple R-squared: 0.5941, Adjusted R-squared: 0.5916
## F-statistic: 234.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.096447 -0.019713 -0.000038 0.026161 0.113788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.632e+00 1.574e-01 10.368 < 2e-16 ***
## time_value -7.254e-05 8.642e-06 -8.394 5.32e-16 ***
## SchoolClose 1.182e+01 3.241e+00 3.648 0.000293 ***
## time_value:SchoolClose -6.387e-04 1.764e-04 -3.621 0.000324 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03559 on 480 degrees of freedom
## Multiple R-squared: 0.3992, Adjusted R-squared: 0.3954
## F-statistic: 106.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.130992 -0.021990 0.000233 0.024309 0.145032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.621e+00 1.769e-01 9.163 < 2e-16 ***
## time_value -7.250e-05 9.724e-06 -7.456 4.21e-13 ***
## SchoolClose 2.268e+01 1.612e+00 14.065 < 2e-16 ***
## time_value:SchoolClose -1.225e-03 8.760e-05 -13.990 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03677 on 480 degrees of freedom
## Multiple R-squared: 0.6543, Adjusted R-squared: 0.6521
## F-statistic: 302.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.092719 -0.018961 -0.001531 0.019823 0.129721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.713e+00 1.381e-01 19.66 <2e-16 ***
## time_value -1.334e-04 7.583e-06 -17.60 <2e-16 ***
## SchoolClose 2.077e+01 2.018e+00 10.29 <2e-16 ***
## time_value:SchoolClose -1.125e-03 1.097e-04 -10.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03048 on 480 degrees of freedom
## Multiple R-squared: 0.576, Adjusted R-squared: 0.5734
## F-statistic: 217.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077478 -0.018602 -0.003131 0.015345 0.146760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.564e+00 1.386e-01 18.498 < 2e-16 ***
## time_value -1.276e-04 7.620e-06 -16.743 < 2e-16 ***
## SchoolClose 1.118e+01 1.432e+00 7.808 3.67e-14 ***
## time_value:SchoolClose -6.047e-04 7.782e-05 -7.770 4.81e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02945 on 480 degrees of freedom
## Multiple R-squared: 0.4751, Adjusted R-squared: 0.4718
## F-statistic: 144.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.093231 -0.020622 -0.001553 0.024193 0.109718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.751e+00 1.508e-01 11.615 < 2e-16 ***
## time_value -7.931e-05 8.282e-06 -9.576 < 2e-16 ***
## SchoolClose 8.667e+00 2.207e+00 3.927 9.86e-05 ***
## time_value:SchoolClose -4.674e-04 1.200e-04 -3.894 0.000112 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03333 on 480 degrees of freedom
## Multiple R-squared: 0.3898, Adjusted R-squared: 0.386
## F-statistic: 102.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.132861 -0.022748 0.000698 0.022739 0.144447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.251e+00 1.604e-01 14.034 < 2e-16 ***
## time_value -1.071e-04 8.812e-06 -12.156 < 2e-16 ***
## SchoolClose 1.878e+01 2.348e+00 7.997 9.64e-15 ***
## time_value:SchoolClose -1.016e-03 1.277e-04 -7.954 1.31e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03546 on 480 degrees of freedom
## Multiple R-squared: 0.5358, Adjusted R-squared: 0.5329
## F-statistic: 184.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.112026 -0.022109 -0.001915 0.019400 0.158744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.692e+00 1.961e-01 13.73 <2e-16 ***
## time_value -1.319e-04 1.080e-05 -12.21 <2e-16 ***
## SchoolClose 1.450e+01 1.131e+00 12.83 <2e-16 ***
## time_value:SchoolClose -7.825e-04 6.138e-05 -12.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03661 on 480 degrees of freedom
## Multiple R-squared: 0.5351, Adjusted R-squared: 0.5322
## F-statistic: 184.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066254 -0.016573 -0.000423 0.017620 0.114973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.893e+00 1.216e-01 15.563 < 2e-16 ***
## time_value -9.001e-05 6.680e-06 -13.476 < 2e-16 ***
## SchoolClose 9.923e+00 1.899e+00 5.225 2.60e-07 ***
## time_value:SchoolClose -5.370e-04 1.033e-04 -5.200 2.96e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02702 on 480 degrees of freedom
## Multiple R-squared: 0.391, Adjusted R-squared: 0.3872
## F-statistic: 102.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.086099 -0.022599 -0.001207 0.016995 0.222047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.994e+00 1.552e-01 19.293 <2e-16 ***
## time_value -1.493e-04 8.513e-06 -17.541 <2e-16 ***
## SchoolClose -1.994e+01 9.073e+00 -2.198 0.0284 *
## time_value:SchoolClose 1.090e-03 4.942e-04 2.205 0.0280 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03611 on 480 degrees of freedom
## Multiple R-squared: 0.4233, Adjusted R-squared: 0.4197
## F-statistic: 117.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.078113 -0.017670 -0.001525 0.017903 0.114123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.282e+00 1.273e-01 17.930 < 2e-16 ***
## time_value -1.112e-04 6.995e-06 -15.896 < 2e-16 ***
## SchoolClose 1.071e+01 1.294e+00 8.276 1.27e-15 ***
## time_value:SchoolClose -5.791e-04 7.033e-05 -8.235 1.72e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02701 on 480 degrees of freedom
## Multiple R-squared: 0.4788, Adjusted R-squared: 0.4756
## F-statistic: 147 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088439 -0.019697 -0.001489 0.019376 0.135402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.662e+00 1.420e-01 11.706 < 2e-16 ***
## time_value -7.672e-05 7.800e-06 -9.836 < 2e-16 ***
## SchoolClose 1.420e+01 1.723e+00 8.242 1.63e-15 ***
## time_value:SchoolClose -7.678e-04 9.365e-05 -8.199 2.23e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0306 on 480 degrees of freedom
## Multiple R-squared: 0.472, Adjusted R-squared: 0.4687
## F-statistic: 143 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052120 -0.015358 -0.002116 0.010201 0.123140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.143e+00 1.929e-01 26.655 < 2e-16 ***
## time_value -2.687e-04 1.065e-05 -25.228 < 2e-16 ***
## SchoolClose 4.095e+00 5.361e-01 7.639 1.20e-13 ***
## time_value:SchoolClose -2.161e-04 2.912e-05 -7.421 5.33e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02406 on 480 degrees of freedom
## Multiple R-squared: 0.6768, Adjusted R-squared: 0.6748
## F-statistic: 335.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.100221 -0.022069 -0.001865 0.024520 0.162643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.150e+00 1.590e-01 7.237 1.83e-12 ***
## time_value -4.764e-05 8.733e-06 -5.455 7.86e-08 ***
## SchoolClose 1.253e+01 2.189e+00 5.723 1.84e-08 ***
## time_value:SchoolClose -6.764e-04 1.190e-04 -5.684 2.28e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03495 on 480 degrees of freedom
## Multiple R-squared: 0.4459, Adjusted R-squared: 0.4424
## F-statistic: 128.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.112862 -0.021501 0.001371 0.022309 0.112998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.042e+00 1.937e-01 20.868 < 2e-16 ***
## time_value -2.052e-04 1.066e-05 -19.246 < 2e-16 ***
## SchoolClose 7.591e+00 1.222e+00 6.212 1.14e-09 ***
## time_value:SchoolClose -4.086e-04 6.634e-05 -6.159 1.55e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03699 on 480 degrees of freedom
## Multiple R-squared: 0.4911, Adjusted R-squared: 0.4879
## F-statistic: 154.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.090349 -0.021275 -0.002784 0.020471 0.200651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.178e+00 1.747e-01 6.739 4.59e-11 ***
## time_value -4.814e-05 9.606e-06 -5.012 7.60e-07 ***
## SchoolClose 1.444e+01 1.652e+00 8.738 < 2e-16 ***
## time_value:SchoolClose -7.800e-04 8.977e-05 -8.689 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03662 on 480 degrees of freedom
## Multiple R-squared: 0.4502, Adjusted R-squared: 0.4468
## F-statistic: 131 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095556 -0.021408 -0.002883 0.020577 0.197383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.687e+00 1.635e-01 16.434 < 2e-16 ***
## time_value -1.316e-04 8.980e-06 -14.656 < 2e-16 ***
## SchoolClose 1.796e+01 2.908e+00 6.175 1.41e-09 ***
## time_value:SchoolClose -9.722e-04 1.582e-04 -6.147 1.66e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03659 on 480 degrees of freedom
## Multiple R-squared: 0.4646, Adjusted R-squared: 0.4612
## F-statistic: 138.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073942 -0.017965 -0.003682 0.015254 0.119852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.366e+00 2.168e-01 29.362 <2e-16 ***
## time_value -3.350e-04 1.197e-05 -27.990 <2e-16 ***
## SchoolClose 1.532e+00 5.954e-01 2.573 0.0104 *
## time_value:SchoolClose -7.791e-05 3.234e-05 -2.409 0.0164 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02691 on 480 degrees of freedom
## Multiple R-squared: 0.6817, Adjusted R-squared: 0.6797
## F-statistic: 342.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * SchoolClose, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066825 -0.018960 -0.002343 0.018527 0.133824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.275e+00 1.294e-01 25.320 < 2e-16 ***
## time_value -1.639e-04 7.102e-06 -23.072 < 2e-16 ***
## SchoolClose 1.172e+01 2.655e+00 4.415 1.25e-05 ***
## time_value:SchoolClose -6.348e-04 1.444e-04 -4.395 1.36e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02917 on 480 degrees of freedom
## Multiple R-squared: 0.5555, Adjusted R-squared: 0.5527
## F-statistic: 200 on 3 and 480 DF, p-value: < 2.2e-16Bar Restriction with full time work prop
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*BarRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.104164 -0.021510 0.001068 0.022752 0.104352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.952e+00 1.428e-01 20.675 <2e-16 ***
## time_value -1.458e-04 7.837e-06 -18.605 <2e-16 ***
## BarRestrict 7.139e+00 4.486e+00 1.591 0.112
## time_value:BarRestrict -3.854e-04 2.442e-04 -1.578 0.115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03278 on 480 degrees of freedom
## Multiple R-squared: 0.458, Adjusted R-squared: 0.4546
## F-statistic: 135.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059189 -0.016808 -0.001801 0.012887 0.156493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.868e+00 2.003e-01 19.306 < 2e-16 ***
## time_value -2.004e-04 1.106e-05 -18.128 < 2e-16 ***
## BarRestrict 3.510e+00 5.771e-01 6.083 2.41e-09 ***
## time_value:BarRestrict -1.865e-04 3.134e-05 -5.951 5.15e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02534 on 480 degrees of freedom
## Multiple R-squared: 0.5116, Adjusted R-squared: 0.5086
## F-statistic: 167.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052218 -0.014728 -0.002232 0.012877 0.141185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.932e+00 1.846e-01 21.298 < 2e-16 ***
## time_value -2.035e-04 1.019e-05 -19.971 < 2e-16 ***
## BarRestrict 3.950e+00 5.318e-01 7.426 5.13e-13 ***
## time_value:BarRestrict -2.105e-04 2.888e-05 -7.289 1.29e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02335 on 480 degrees of freedom
## Multiple R-squared: 0.5707, Adjusted R-squared: 0.568
## F-statistic: 212.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060274 -0.017763 -0.001797 0.014070 0.107383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.066e+00 2.123e-01 19.157 < 2e-16 ***
## time_value -2.074e-04 1.171e-05 -17.707 < 2e-16 ***
## BarRestrict 4.593e+00 6.751e-01 6.803 3.06e-11 ***
## time_value:BarRestrict -2.437e-04 3.664e-05 -6.653 7.86e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02786 on 480 degrees of freedom
## Multiple R-squared: 0.5357, Adjusted R-squared: 0.5328
## F-statistic: 184.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063925 -0.014489 -0.001801 0.010993 0.115345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.895e+00 1.929e-01 25.37 <2e-16 ***
## time_value -2.545e-04 1.065e-05 -23.90 <2e-16 ***
## BarRestrict 5.804e+00 5.361e-01 10.82 <2e-16 ***
## time_value:BarRestrict -3.056e-04 2.912e-05 -10.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02406 on 480 degrees of freedom
## Multiple R-squared: 0.8182, Adjusted R-squared: 0.8171
## F-statistic: 720.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060142 -0.019249 -0.004789 0.012963 0.182213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.866e+00 2.526e-01 19.263 <2e-16 ***
## time_value -2.519e-04 1.394e-05 -18.069 <2e-16 ***
## BarRestrict 6.646e+00 7.103e-01 9.357 <2e-16 ***
## time_value:BarRestrict -3.542e-04 3.858e-05 -9.183 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03165 on 480 degrees of freedom
## Multiple R-squared: 0.5819, Adjusted R-squared: 0.5793
## F-statistic: 222.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080135 -0.020918 -0.005272 0.014343 0.168107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.675e+00 2.654e-01 17.62 <2e-16 ***
## time_value -2.425e-04 1.465e-05 -16.56 <2e-16 ***
## BarRestrict 9.827e+00 7.374e-01 13.33 <2e-16 ***
## time_value:BarRestrict -5.250e-04 4.005e-05 -13.11 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03309 on 480 degrees of freedom
## Multiple R-squared: 0.6749, Adjusted R-squared: 0.6729
## F-statistic: 332.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08397 -0.01649 -0.00174 0.01388 0.18295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.545e+00 2.220e-01 11.46 <2e-16 ***
## time_value -1.235e-04 1.225e-05 -10.08 <2e-16 ***
## BarRestrict 8.738e+00 6.168e-01 14.17 <2e-16 ***
## time_value:BarRestrict -4.657e-04 3.350e-05 -13.90 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02768 on 480 degrees of freedom
## Multiple R-squared: 0.8011, Adjusted R-squared: 0.7999
## F-statistic: 644.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073735 -0.018117 -0.004104 0.015344 0.127769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.847e+00 2.237e-01 21.667 <2e-16 ***
## time_value -2.509e-04 1.235e-05 -20.318 <2e-16 ***
## BarRestrict 6.053e+00 6.216e-01 9.737 <2e-16 ***
## time_value:BarRestrict -3.214e-04 3.376e-05 -9.518 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0279 on 480 degrees of freedom
## Multiple R-squared: 0.6609, Adjusted R-squared: 0.6587
## F-statistic: 311.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088707 -0.016301 -0.000972 0.015745 0.132410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.105e+00 1.642e-01 12.82 <2e-16 ***
## time_value -1.007e-04 9.042e-06 -11.14 <2e-16 ***
## BarRestrict 8.675e+00 8.251e-01 10.52 <2e-16 ***
## time_value:BarRestrict -4.665e-04 4.478e-05 -10.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02905 on 480 degrees of freedom
## Multiple R-squared: 0.5391, Adjusted R-squared: 0.5363
## F-statistic: 187.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051222 -0.016673 -0.002225 0.012091 0.139103
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.500e+00 2.069e-01 16.915 <2e-16 ***
## time_value -1.789e-04 1.142e-05 -15.668 <2e-16 ***
## BarRestrict 6.022e+00 6.182e-01 9.741 <2e-16 ***
## time_value:BarRestrict -3.217e-04 3.356e-05 -9.585 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02654 on 480 degrees of freedom
## Multiple R-squared: 0.5476, Adjusted R-squared: 0.5448
## F-statistic: 193.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.087968 -0.016146 -0.000166 0.014362 0.120977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.685e+00 1.967e-01 23.824 <2e-16 ***
## time_value -2.444e-04 1.085e-05 -22.511 <2e-16 ***
## BarRestrict 1.378e+00 5.530e-01 2.493 0.013 *
## time_value:BarRestrict -6.665e-05 3.003e-05 -2.219 0.027 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02464 on 480 degrees of freedom
## Multiple R-squared: 0.7457, Adjusted R-squared: 0.7442
## F-statistic: 469.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057830 -0.020368 -0.004502 0.014197 0.167545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.604e+00 2.363e-01 23.709 < 2e-16 ***
## time_value -2.948e-04 1.305e-05 -22.596 < 2e-16 ***
## BarRestrict 5.464e+00 6.646e-01 8.221 1.90e-15 ***
## time_value:BarRestrict -2.907e-04 3.609e-05 -8.055 6.33e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02962 on 480 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6267
## F-statistic: 271.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049648 -0.016227 -0.003432 0.013605 0.135389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.046e+00 1.882e-01 26.814 < 2e-16 ***
## time_value -2.621e-04 1.038e-05 -25.244 < 2e-16 ***
## BarRestrict 3.142e+00 5.692e-01 5.520 5.54e-08 ***
## time_value:BarRestrict -1.660e-04 3.090e-05 -5.373 1.21e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02425 on 480 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6437
## F-statistic: 291.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070745 -0.020444 -0.003379 0.016081 0.211759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.009e+00 2.346e-01 21.35 <2e-16 ***
## time_value -2.610e-04 1.295e-05 -20.16 <2e-16 ***
## BarRestrict 7.494e+00 6.519e-01 11.49 <2e-16 ***
## time_value:BarRestrict -3.989e-04 3.541e-05 -11.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02926 on 480 degrees of freedom
## Multiple R-squared: 0.6875, Adjusted R-squared: 0.6855
## F-statistic: 352 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056975 -0.018389 -0.003983 0.013965 0.164268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.763e+00 2.125e-01 22.41 <2e-16 ***
## time_value -2.476e-04 1.173e-05 -21.11 <2e-16 ***
## BarRestrict 6.422e+00 5.905e-01 10.88 <2e-16 ***
## time_value:BarRestrict -3.433e-04 3.207e-05 -10.70 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0265 on 480 degrees of freedom
## Multiple R-squared: 0.636, Adjusted R-squared: 0.6337
## F-statistic: 279.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088236 -0.021784 -0.000508 0.018972 0.128253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.469e+00 1.392e-01 17.737 < 2e-16 ***
## time_value -1.209e-04 7.639e-06 -15.828 < 2e-16 ***
## BarRestrict 2.214e+01 5.352e+00 4.137 4.16e-05 ***
## time_value:BarRestrict -1.200e-03 2.912e-04 -4.119 4.47e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.032 on 480 degrees of freedom
## Multiple R-squared: 0.4946, Adjusted R-squared: 0.4915
## F-statistic: 156.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047735 -0.018851 -0.002091 0.014601 0.131351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.702e+00 2.023e-01 28.179 < 2e-16 ***
## time_value -2.995e-04 1.117e-05 -26.813 < 2e-16 ***
## BarRestrict 2.526e+00 5.622e-01 4.492 8.83e-06 ***
## time_value:BarRestrict -1.316e-04 3.054e-05 -4.310 1.98e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02524 on 480 degrees of freedom
## Multiple R-squared: 0.665, Adjusted R-squared: 0.6629
## F-statistic: 317.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057449 -0.016443 -0.001263 0.013595 0.112812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.043e+00 1.891e-01 21.381 <2e-16 ***
## time_value -2.091e-04 1.044e-05 -20.029 <2e-16 ***
## BarRestrict 5.044e+00 5.317e-01 9.487 <2e-16 ***
## time_value:BarRestrict -2.692e-04 2.888e-05 -9.320 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0237 on 480 degrees of freedom
## Multiple R-squared: 0.6012, Adjusted R-squared: 0.5987
## F-statistic: 241.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076225 -0.020727 -0.004651 0.013421 0.138828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.550e+00 2.564e-01 17.75 <2e-16 ***
## time_value -2.352e-04 1.415e-05 -16.62 <2e-16 ***
## BarRestrict 9.134e+00 7.209e-01 12.67 <2e-16 ***
## time_value:BarRestrict -4.868e-04 3.915e-05 -12.43 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03212 on 480 degrees of freedom
## Multiple R-squared: 0.7129, Adjusted R-squared: 0.7111
## F-statistic: 397.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089544 -0.018273 -0.003829 0.012663 0.227040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.564e+00 2.460e-01 18.55 <2e-16 ***
## time_value -2.358e-04 1.358e-05 -17.36 <2e-16 ***
## BarRestrict 6.933e+00 6.837e-01 10.14 <2e-16 ***
## time_value:BarRestrict -3.673e-04 3.713e-05 -9.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03068 on 480 degrees of freedom
## Multiple R-squared: 0.7245, Adjusted R-squared: 0.7227
## F-statistic: 420.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061661 -0.021025 -0.003151 0.014435 0.161270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.390e+00 2.449e-01 22.004 < 2e-16 ***
## time_value -2.803e-04 1.352e-05 -20.735 < 2e-16 ***
## BarRestrict 4.035e+00 6.971e-01 5.788 1.29e-08 ***
## time_value:BarRestrict -2.141e-04 3.786e-05 -5.656 2.67e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03084 on 480 degrees of freedom
## Multiple R-squared: 0.5719, Adjusted R-squared: 0.5693
## F-statistic: 213.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.106337 -0.022231 -0.005748 0.018592 0.161859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.499e+00 2.111e-01 16.57 <2e-16 ***
## time_value -1.766e-04 1.163e-05 -15.18 <2e-16 ***
## BarRestrict 1.400e+01 9.630e-01 14.53 <2e-16 ***
## time_value:BarRestrict -7.547e-04 5.227e-05 -14.44 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03595 on 480 degrees of freedom
## Multiple R-squared: 0.5695, Adjusted R-squared: 0.5668
## F-statistic: 211.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063060 -0.021987 -0.004503 0.015352 0.152742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.984e+00 2.550e-01 19.546 < 2e-16 ***
## time_value -2.593e-04 1.407e-05 -18.425 < 2e-16 ***
## BarRestrict 6.060e+00 7.170e-01 8.452 3.45e-16 ***
## time_value:BarRestrict -3.220e-04 3.894e-05 -8.269 1.34e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03195 on 480 degrees of freedom
## Multiple R-squared: 0.5893, Adjusted R-squared: 0.5867
## F-statistic: 229.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058779 -0.019268 -0.004401 0.012170 0.165095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.513e+00 2.241e-01 20.141 < 2e-16 ***
## time_value -2.340e-04 1.237e-05 -18.922 < 2e-16 ***
## BarRestrict 5.584e+00 6.613e-01 8.445 3.65e-16 ***
## time_value:BarRestrict -2.981e-04 3.590e-05 -8.302 1.05e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02861 on 480 degrees of freedom
## Multiple R-squared: 0.5591, Adjusted R-squared: 0.5564
## F-statistic: 202.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049924 -0.015644 -0.002702 0.012486 0.144295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.244e+00 1.904e-01 17.037 < 2e-16 ***
## time_value -1.660e-04 1.051e-05 -15.804 < 2e-16 ***
## BarRestrict 4.054e+00 5.688e-01 7.128 3.77e-12 ***
## time_value:BarRestrict -2.168e-04 3.088e-05 -7.022 7.52e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02442 on 480 degrees of freedom
## Multiple R-squared: 0.4708, Adjusted R-squared: 0.4675
## F-statistic: 142.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05289 -0.01785 -0.00195 0.01470 0.11772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.745e+00 2.076e-01 22.860 < 2e-16 ***
## time_value -2.455e-04 1.146e-05 -21.430 < 2e-16 ***
## BarRestrict 2.153e+00 6.052e-01 3.558 0.000411 ***
## time_value:BarRestrict -1.129e-04 3.286e-05 -3.436 0.000643 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02638 on 480 degrees of freedom
## Multiple R-squared: 0.5655, Adjusted R-squared: 0.5628
## F-statistic: 208.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058415 -0.014169 -0.001423 0.014318 0.117519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.829e+00 1.907e-01 20.077 < 2e-16 ***
## time_value -1.965e-04 1.053e-05 -18.666 < 2e-16 ***
## BarRestrict 4.294e+00 5.363e-01 8.008 8.91e-15 ***
## time_value:BarRestrict -2.280e-04 2.913e-05 -7.829 3.18e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0239 on 480 degrees of freedom
## Multiple R-squared: 0.5806, Adjusted R-squared: 0.578
## F-statistic: 221.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.094994 -0.019990 0.000978 0.021268 0.140990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.919e+00 1.503e-01 19.426 <2e-16 ***
## time_value -1.452e-04 8.246e-06 -17.610 <2e-16 ***
## BarRestrict 1.183e+01 5.040e+00 2.347 0.0193 *
## time_value:BarRestrict -6.399e-04 2.743e-04 -2.333 0.0201 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03452 on 480 degrees of freedom
## Multiple R-squared: 0.4577, Adjusted R-squared: 0.4543
## F-statistic: 135 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076064 -0.022140 -0.002974 0.017037 0.155924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.663e+00 1.782e-01 20.55 <2e-16 ***
## time_value -1.867e-04 9.807e-06 -19.04 <2e-16 ***
## BarRestrict 1.267e+01 1.235e+00 10.26 <2e-16 ***
## time_value:BarRestrict -6.841e-04 6.698e-05 -10.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03389 on 480 degrees of freedom
## Multiple R-squared: 0.5283, Adjusted R-squared: 0.5253
## F-statistic: 179.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.108868 -0.020836 -0.000162 0.020462 0.165840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.420e+00 1.789e-01 13.528 <2e-16 ***
## time_value -1.174e-04 9.839e-06 -11.931 <2e-16 ***
## BarRestrict 1.328e+01 1.339e+00 9.918 <2e-16 ***
## time_value:BarRestrict -7.170e-04 7.273e-05 -9.858 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0359 on 480 degrees of freedom
## Multiple R-squared: 0.4795, Adjusted R-squared: 0.4763
## F-statistic: 147.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083629 -0.021404 -0.005091 0.013902 0.163997
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.966e+00 2.754e-01 18.03 <2e-16 ***
## time_value -2.591e-04 1.520e-05 -17.04 <2e-16 ***
## BarRestrict 1.288e+01 7.654e-01 16.82 <2e-16 ***
## time_value:BarRestrict -6.881e-04 4.157e-05 -16.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03435 on 480 degrees of freedom
## Multiple R-squared: 0.7738, Adjusted R-squared: 0.7724
## F-statistic: 547.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060151 -0.016470 -0.002778 0.012387 0.140710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.966e+00 2.034e-01 24.416 <2e-16 ***
## time_value -2.573e-04 1.123e-05 -22.921 <2e-16 ***
## BarRestrict 8.237e-01 5.858e-01 1.406 0.160
## time_value:BarRestrict -3.902e-05 3.181e-05 -1.227 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02573 on 480 degrees of freedom
## Multiple R-squared: 0.582, Adjusted R-squared: 0.5794
## F-statistic: 222.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055035 -0.015897 -0.002439 0.014451 0.133383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.091e+00 2.039e-01 24.972 < 2e-16 ***
## time_value -2.639e-04 1.125e-05 -23.455 < 2e-16 ***
## BarRestrict 5.026e+00 5.944e-01 8.455 3.38e-16 ***
## time_value:BarRestrict -2.657e-04 3.227e-05 -8.231 1.76e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02591 on 480 degrees of freedom
## Multiple R-squared: 0.6816, Adjusted R-squared: 0.6796
## F-statistic: 342.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.103189 -0.020078 -0.003337 0.015194 0.151693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.074e+00 2.450e-01 20.71 <2e-16 ***
## time_value -2.635e-04 1.353e-05 -19.48 <2e-16 ***
## BarRestrict 1.063e+01 6.809e-01 15.62 <2e-16 ***
## time_value:BarRestrict -5.675e-04 3.698e-05 -15.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03056 on 480 degrees of freedom
## Multiple R-squared: 0.7613, Adjusted R-squared: 0.7598
## F-statistic: 510.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.084731 -0.019234 -0.003863 0.014510 0.115198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.225e+00 2.210e-01 23.64 <2e-16 ***
## time_value -2.724e-04 1.220e-05 -22.33 <2e-16 ***
## BarRestrict 6.520e+00 6.070e-01 10.74 <2e-16 ***
## time_value:BarRestrict -3.479e-04 3.297e-05 -10.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02743 on 480 degrees of freedom
## Multiple R-squared: 0.6565, Adjusted R-squared: 0.6543
## F-statistic: 305.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08314 -0.01851 -0.00263 0.01455 0.14100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.205e+00 1.328e-01 16.603 < 2e-16 ***
## time_value -1.076e-04 7.289e-06 -14.765 < 2e-16 ***
## BarRestrict 3.094e+01 4.740e+00 6.526 1.71e-10 ***
## time_value:BarRestrict -1.679e-03 2.579e-04 -6.512 1.87e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03044 on 480 degrees of freedom
## Multiple R-squared: 0.4392, Adjusted R-squared: 0.4357
## F-statistic: 125.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069125 -0.016077 -0.001762 0.013409 0.126173
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.073e+00 1.947e-01 26.052 < 2e-16 ***
## time_value -2.631e-04 1.075e-05 -24.476 < 2e-16 ***
## BarRestrict 3.181e+00 5.475e-01 5.809 1.14e-08 ***
## time_value:BarRestrict -1.660e-04 2.974e-05 -5.581 4.00e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0244 on 480 degrees of freedom
## Multiple R-squared: 0.6729, Adjusted R-squared: 0.6709
## F-statistic: 329.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070717 -0.019215 -0.004558 0.014963 0.121998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.398e+00 2.343e-01 23.04 <2e-16 ***
## time_value -2.812e-04 1.293e-05 -21.74 <2e-16 ***
## BarRestrict 7.819e+00 6.589e-01 11.87 <2e-16 ***
## time_value:BarRestrict -4.169e-04 3.579e-05 -11.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02936 on 480 degrees of freedom
## Multiple R-squared: 0.6817, Adjusted R-squared: 0.6797
## F-statistic: 342.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079615 -0.020333 -0.004588 0.014037 0.140250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.442e+00 2.506e-01 21.72 <2e-16 ***
## time_value -2.840e-04 1.383e-05 -20.53 <2e-16 ***
## BarRestrict 7.200e+00 7.046e-01 10.22 <2e-16 ***
## time_value:BarRestrict -3.829e-04 3.827e-05 -10.01 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0314 on 480 degrees of freedom
## Multiple R-squared: 0.6579, Adjusted R-squared: 0.6558
## F-statistic: 307.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049086 -0.014254 -0.000908 0.013272 0.127693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.880e+00 1.831e-01 21.195 < 2e-16 ***
## time_value -2.000e-04 1.010e-05 -19.790 < 2e-16 ***
## BarRestrict 3.394e+00 5.210e-01 6.515 1.83e-10 ***
## time_value:BarRestrict -1.800e-04 2.829e-05 -6.361 4.67e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02305 on 480 degrees of freedom
## Multiple R-squared: 0.5569, Adjusted R-squared: 0.5542
## F-statistic: 201.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088113 -0.021948 -0.001727 0.016525 0.221611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.908e+00 1.580e-01 18.408 <2e-16 ***
## time_value -1.445e-04 8.666e-06 -16.681 <2e-16 ***
## BarRestrict 4.222e+01 2.407e+01 1.754 0.0800 .
## time_value:BarRestrict -2.295e-03 1.310e-03 -1.751 0.0805 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03697 on 480 degrees of freedom
## Multiple R-squared: 0.3955, Adjusted R-squared: 0.3917
## F-statistic: 104.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063246 -0.016894 -0.001877 0.015976 0.109478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.744e+00 1.505e-01 18.23 <2e-16 ***
## time_value -1.368e-04 8.293e-06 -16.49 <2e-16 ***
## BarRestrict 8.262e+00 7.412e-01 11.15 <2e-16 ***
## time_value:BarRestrict -4.453e-04 4.022e-05 -11.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02606 on 480 degrees of freedom
## Multiple R-squared: 0.5149, Adjusted R-squared: 0.5119
## F-statistic: 169.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059293 -0.015903 -0.002166 0.010722 0.114169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.106e+00 1.989e-01 20.64 <2e-16 ***
## time_value -2.119e-04 1.098e-05 -19.31 <2e-16 ***
## BarRestrict 6.110e+00 5.799e-01 10.54 <2e-16 ***
## time_value:BarRestrict -3.255e-04 3.149e-05 -10.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02527 on 480 degrees of freedom
## Multiple R-squared: 0.6399, Adjusted R-squared: 0.6376
## F-statistic: 284.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051911 -0.015272 -0.002355 0.010377 0.120552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.922e+00 1.981e-01 24.851 < 2e-16 ***
## time_value -2.564e-04 1.093e-05 -23.459 < 2e-16 ***
## BarRestrict 4.624e+00 5.705e-01 8.106 4.39e-15 ***
## time_value:BarRestrict -2.450e-04 3.098e-05 -7.909 1.80e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02505 on 480 degrees of freedom
## Multiple R-squared: 0.6497, Adjusted R-squared: 0.6475
## F-statistic: 296.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061111 -0.017894 -0.003503 0.013220 0.141458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.999e+00 2.230e-01 17.931 <2e-16 ***
## time_value -2.052e-04 1.231e-05 -16.672 <2e-16 ***
## BarRestrict 6.549e+00 6.745e-01 9.709 <2e-16 ***
## time_value:BarRestrict -3.486e-04 3.662e-05 -9.519 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02874 on 480 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6231
## F-statistic: 267.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.085054 -0.021188 -0.003169 0.018239 0.115283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.621e+00 2.544e-01 26.026 < 2e-16 ***
## time_value -3.478e-04 1.404e-05 -24.771 < 2e-16 ***
## BarRestrict 2.393e+00 7.153e-01 3.345 0.000888 ***
## time_value:BarRestrict -1.237e-04 3.885e-05 -3.184 0.001546 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03188 on 480 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6198
## F-statistic: 263.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063874 -0.018834 -0.003506 0.015372 0.174236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.605e+00 2.329e-01 19.767 < 2e-16 ***
## time_value -2.377e-04 1.286e-05 -18.489 < 2e-16 ***
## BarRestrict 4.480e+00 6.473e-01 6.922 1.44e-11 ***
## time_value:BarRestrict -2.356e-04 3.516e-05 -6.700 5.85e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02905 on 480 degrees of freedom
## Multiple R-squared: 0.654, Adjusted R-squared: 0.6519
## F-statistic: 302.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.099428 -0.023029 -0.002697 0.022294 0.198592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.437e+00 1.647e-01 14.802 <2e-16 ***
## time_value -1.177e-04 9.036e-06 -13.028 <2e-16 ***
## BarRestrict -2.336e+00 6.343e+00 -0.368 0.713
## time_value:BarRestrict 1.326e-04 3.454e-04 0.384 0.701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03802 on 480 degrees of freedom
## Multiple R-squared: 0.4219, Adjusted R-squared: 0.4183
## F-statistic: 116.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066171 -0.018287 -0.004128 0.015322 0.118210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.258e+00 2.126e-01 29.435 < 2e-16 ***
## time_value -3.290e-04 1.173e-05 -28.037 < 2e-16 ***
## BarRestrict 2.128e+00 6.050e-01 3.516 0.000479 ***
## time_value:BarRestrict -1.102e-04 3.286e-05 -3.355 0.000856 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02677 on 480 degrees of freedom
## Multiple R-squared: 0.6851, Adjusted R-squared: 0.6831
## F-statistic: 348.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * BarRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055959 -0.017552 -0.004304 0.013083 0.124670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.035e+00 2.110e-01 23.866 < 2e-16 ***
## time_value -2.612e-04 1.164e-05 -22.435 < 2e-16 ***
## BarRestrict 3.798e+00 6.076e-01 6.251 9.01e-10 ***
## time_value:BarRestrict -2.023e-04 3.300e-05 -6.130 1.84e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02668 on 480 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6257
## F-statistic: 270.1 on 3 and 480 DF, p-value: < 2.2e-16Restaurant Restriction with full time work prop
# The fraction of mobile devices that did not leave the immediate area of their home (SafeGraph’s completely_home_device_count / device_count)
for(state in states){
print(state)
print("---------------------")
chome.state <- chome %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(chome.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
lm.fit <- lm(value~time_value*RestaurantRestrict,data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.104164 -0.021510 0.001068 0.022752 0.104352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.952e+00 1.428e-01 20.675 <2e-16 ***
## time_value -1.458e-04 7.837e-06 -18.605 <2e-16 ***
## RestaurantRestrict 7.139e+00 4.486e+00 1.591 0.112
## time_value:RestaurantRestrict -3.854e-04 2.442e-04 -1.578 0.115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03278 on 480 degrees of freedom
## Multiple R-squared: 0.458, Adjusted R-squared: 0.4546
## F-statistic: 135.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "al"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059189 -0.016808 -0.001801 0.012887 0.156493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.868e+00 2.003e-01 19.306 < 2e-16 ***
## time_value -2.004e-04 1.106e-05 -18.128 < 2e-16 ***
## RestaurantRestrict 3.510e+00 5.771e-01 6.083 2.41e-09 ***
## time_value:RestaurantRestrict -1.865e-04 3.134e-05 -5.951 5.15e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02534 on 480 degrees of freedom
## Multiple R-squared: 0.5116, Adjusted R-squared: 0.5086
## F-statistic: 167.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ar"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052218 -0.014728 -0.002232 0.012877 0.141185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.932e+00 1.846e-01 21.298 < 2e-16 ***
## time_value -2.035e-04 1.019e-05 -19.971 < 2e-16 ***
## RestaurantRestrict 3.950e+00 5.318e-01 7.426 5.13e-13 ***
## time_value:RestaurantRestrict -2.105e-04 2.888e-05 -7.289 1.29e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02335 on 480 degrees of freedom
## Multiple R-squared: 0.5707, Adjusted R-squared: 0.568
## F-statistic: 212.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "az"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060274 -0.017763 -0.001797 0.014070 0.107383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.066e+00 2.123e-01 19.157 < 2e-16 ***
## time_value -2.074e-04 1.171e-05 -17.707 < 2e-16 ***
## RestaurantRestrict 4.593e+00 6.751e-01 6.803 3.06e-11 ***
## time_value:RestaurantRestrict -2.437e-04 3.664e-05 -6.653 7.86e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02786 on 480 degrees of freedom
## Multiple R-squared: 0.5357, Adjusted R-squared: 0.5328
## F-statistic: 184.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ca"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063925 -0.014489 -0.001801 0.010993 0.115345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.895e+00 1.929e-01 25.37 <2e-16 ***
## time_value -2.545e-04 1.065e-05 -23.90 <2e-16 ***
## RestaurantRestrict 5.804e+00 5.361e-01 10.82 <2e-16 ***
## time_value:RestaurantRestrict -3.056e-04 2.912e-05 -10.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02406 on 480 degrees of freedom
## Multiple R-squared: 0.8182, Adjusted R-squared: 0.8171
## F-statistic: 720.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "co"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060142 -0.019249 -0.004789 0.012963 0.182213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.866e+00 2.526e-01 19.263 <2e-16 ***
## time_value -2.519e-04 1.394e-05 -18.069 <2e-16 ***
## RestaurantRestrict 6.646e+00 7.103e-01 9.357 <2e-16 ***
## time_value:RestaurantRestrict -3.542e-04 3.858e-05 -9.183 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03165 on 480 degrees of freedom
## Multiple R-squared: 0.5819, Adjusted R-squared: 0.5793
## F-statistic: 222.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ct"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080135 -0.020918 -0.005272 0.014343 0.168107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.675e+00 2.654e-01 17.62 <2e-16 ***
## time_value -2.425e-04 1.465e-05 -16.56 <2e-16 ***
## RestaurantRestrict 9.827e+00 7.374e-01 13.33 <2e-16 ***
## time_value:RestaurantRestrict -5.250e-04 4.005e-05 -13.11 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03309 on 480 degrees of freedom
## Multiple R-squared: 0.6749, Adjusted R-squared: 0.6729
## F-statistic: 332.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "dc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08397 -0.01649 -0.00174 0.01388 0.18295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.545e+00 2.220e-01 11.46 <2e-16 ***
## time_value -1.235e-04 1.225e-05 -10.08 <2e-16 ***
## RestaurantRestrict 8.738e+00 6.168e-01 14.17 <2e-16 ***
## time_value:RestaurantRestrict -4.657e-04 3.350e-05 -13.90 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02768 on 480 degrees of freedom
## Multiple R-squared: 0.8011, Adjusted R-squared: 0.7999
## F-statistic: 644.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "de"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073735 -0.018117 -0.004104 0.015344 0.127769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.847e+00 2.237e-01 21.667 <2e-16 ***
## time_value -2.509e-04 1.235e-05 -20.318 <2e-16 ***
## RestaurantRestrict 6.053e+00 6.216e-01 9.737 <2e-16 ***
## time_value:RestaurantRestrict -3.214e-04 3.376e-05 -9.518 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0279 on 480 degrees of freedom
## Multiple R-squared: 0.6609, Adjusted R-squared: 0.6587
## F-statistic: 311.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "fl"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088707 -0.016301 -0.000972 0.015745 0.132410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.105e+00 1.642e-01 12.82 <2e-16 ***
## time_value -1.007e-04 9.042e-06 -11.14 <2e-16 ***
## RestaurantRestrict 8.675e+00 8.251e-01 10.52 <2e-16 ***
## time_value:RestaurantRestrict -4.665e-04 4.478e-05 -10.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02905 on 480 degrees of freedom
## Multiple R-squared: 0.5391, Adjusted R-squared: 0.5363
## F-statistic: 187.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ga"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060884 -0.018203 -0.004097 0.011850 0.139693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.787e+00 2.315e-01 12.037 < 2e-16 ***
## time_value -1.394e-04 1.277e-05 -10.913 < 2e-16 ***
## RestaurantRestrict 6.507e+00 7.653e-01 8.503 2.37e-16 ***
## time_value:RestaurantRestrict -3.489e-04 4.152e-05 -8.403 4.97e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0308 on 480 degrees of freedom
## Multiple R-squared: 0.3908, Adjusted R-squared: 0.387
## F-statistic: 102.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "hi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.087968 -0.016146 -0.000166 0.014362 0.120977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.685e+00 1.967e-01 23.824 <2e-16 ***
## time_value -2.444e-04 1.085e-05 -22.511 <2e-16 ***
## RestaurantRestrict 1.378e+00 5.530e-01 2.493 0.013 *
## time_value:RestaurantRestrict -6.665e-05 3.003e-05 -2.219 0.027 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02464 on 480 degrees of freedom
## Multiple R-squared: 0.7457, Adjusted R-squared: 0.7442
## F-statistic: 469.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ia"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057830 -0.020368 -0.004502 0.014197 0.167545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.604e+00 2.363e-01 23.709 < 2e-16 ***
## time_value -2.948e-04 1.305e-05 -22.596 < 2e-16 ***
## RestaurantRestrict 5.464e+00 6.646e-01 8.221 1.90e-15 ***
## time_value:RestaurantRestrict -2.907e-04 3.609e-05 -8.055 6.33e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02962 on 480 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6267
## F-statistic: 271.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "id"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049648 -0.016227 -0.003432 0.013605 0.135389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.046e+00 1.882e-01 26.814 < 2e-16 ***
## time_value -2.621e-04 1.038e-05 -25.244 < 2e-16 ***
## RestaurantRestrict 3.142e+00 5.692e-01 5.520 5.54e-08 ***
## time_value:RestaurantRestrict -1.660e-04 3.090e-05 -5.373 1.21e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02425 on 480 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6437
## F-statistic: 291.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "il"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070745 -0.020444 -0.003379 0.016081 0.211759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.009e+00 2.346e-01 21.35 <2e-16 ***
## time_value -2.610e-04 1.295e-05 -20.16 <2e-16 ***
## RestaurantRestrict 7.494e+00 6.519e-01 11.49 <2e-16 ***
## time_value:RestaurantRestrict -3.989e-04 3.541e-05 -11.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02926 on 480 degrees of freedom
## Multiple R-squared: 0.6875, Adjusted R-squared: 0.6855
## F-statistic: 352 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "in"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056975 -0.018389 -0.003983 0.013965 0.164268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.763e+00 2.125e-01 22.41 <2e-16 ***
## time_value -2.476e-04 1.173e-05 -21.11 <2e-16 ***
## RestaurantRestrict 6.422e+00 5.905e-01 10.88 <2e-16 ***
## time_value:RestaurantRestrict -3.433e-04 3.207e-05 -10.70 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0265 on 480 degrees of freedom
## Multiple R-squared: 0.636, Adjusted R-squared: 0.6337
## F-statistic: 279.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ks"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088236 -0.021784 -0.000508 0.018972 0.128253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.469e+00 1.392e-01 17.737 < 2e-16 ***
## time_value -1.209e-04 7.639e-06 -15.828 < 2e-16 ***
## RestaurantRestrict 2.214e+01 5.352e+00 4.137 4.16e-05 ***
## time_value:RestaurantRestrict -1.200e-03 2.912e-04 -4.119 4.47e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.032 on 480 degrees of freedom
## Multiple R-squared: 0.4946, Adjusted R-squared: 0.4915
## F-statistic: 156.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ky"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.047735 -0.018851 -0.002091 0.014601 0.131351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.702e+00 2.023e-01 28.179 < 2e-16 ***
## time_value -2.995e-04 1.117e-05 -26.813 < 2e-16 ***
## RestaurantRestrict 2.526e+00 5.622e-01 4.492 8.83e-06 ***
## time_value:RestaurantRestrict -1.316e-04 3.054e-05 -4.310 1.98e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02524 on 480 degrees of freedom
## Multiple R-squared: 0.665, Adjusted R-squared: 0.6629
## F-statistic: 317.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "la"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.057449 -0.016443 -0.001263 0.013595 0.112812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.043e+00 1.891e-01 21.381 <2e-16 ***
## time_value -2.091e-04 1.044e-05 -20.029 <2e-16 ***
## RestaurantRestrict 5.044e+00 5.317e-01 9.487 <2e-16 ***
## time_value:RestaurantRestrict -2.692e-04 2.888e-05 -9.320 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0237 on 480 degrees of freedom
## Multiple R-squared: 0.6012, Adjusted R-squared: 0.5987
## F-statistic: 241.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ma"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076225 -0.020727 -0.004651 0.013421 0.138828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.550e+00 2.564e-01 17.75 <2e-16 ***
## time_value -2.352e-04 1.415e-05 -16.62 <2e-16 ***
## RestaurantRestrict 9.134e+00 7.209e-01 12.67 <2e-16 ***
## time_value:RestaurantRestrict -4.868e-04 3.915e-05 -12.43 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03212 on 480 degrees of freedom
## Multiple R-squared: 0.7129, Adjusted R-squared: 0.7111
## F-statistic: 397.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "md"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089544 -0.018273 -0.003829 0.012663 0.227040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.564e+00 2.460e-01 18.55 <2e-16 ***
## time_value -2.358e-04 1.358e-05 -17.36 <2e-16 ***
## RestaurantRestrict 6.933e+00 6.837e-01 10.14 <2e-16 ***
## time_value:RestaurantRestrict -3.673e-04 3.713e-05 -9.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03068 on 480 degrees of freedom
## Multiple R-squared: 0.7245, Adjusted R-squared: 0.7227
## F-statistic: 420.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "me"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.061661 -0.021025 -0.003151 0.014435 0.161270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.390e+00 2.449e-01 22.004 < 2e-16 ***
## time_value -2.803e-04 1.352e-05 -20.735 < 2e-16 ***
## RestaurantRestrict 4.035e+00 6.971e-01 5.788 1.29e-08 ***
## time_value:RestaurantRestrict -2.141e-04 3.786e-05 -5.656 2.67e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03084 on 480 degrees of freedom
## Multiple R-squared: 0.5719, Adjusted R-squared: 0.5693
## F-statistic: 213.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.106337 -0.022231 -0.005748 0.018592 0.161859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.499e+00 2.111e-01 16.57 <2e-16 ***
## time_value -1.766e-04 1.163e-05 -15.18 <2e-16 ***
## RestaurantRestrict 1.400e+01 9.630e-01 14.53 <2e-16 ***
## time_value:RestaurantRestrict -7.547e-04 5.227e-05 -14.44 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03595 on 480 degrees of freedom
## Multiple R-squared: 0.5695, Adjusted R-squared: 0.5668
## F-statistic: 211.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063060 -0.021987 -0.004503 0.015352 0.152742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.984e+00 2.550e-01 19.546 < 2e-16 ***
## time_value -2.593e-04 1.407e-05 -18.425 < 2e-16 ***
## RestaurantRestrict 6.060e+00 7.170e-01 8.452 3.45e-16 ***
## time_value:RestaurantRestrict -3.220e-04 3.894e-05 -8.269 1.34e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03195 on 480 degrees of freedom
## Multiple R-squared: 0.5893, Adjusted R-squared: 0.5867
## F-statistic: 229.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mo"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058779 -0.019268 -0.004401 0.012170 0.165095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.513e+00 2.241e-01 20.141 < 2e-16 ***
## time_value -2.340e-04 1.237e-05 -18.922 < 2e-16 ***
## RestaurantRestrict 5.584e+00 6.613e-01 8.445 3.65e-16 ***
## time_value:RestaurantRestrict -2.981e-04 3.590e-05 -8.302 1.05e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02861 on 480 degrees of freedom
## Multiple R-squared: 0.5591, Adjusted R-squared: 0.5564
## F-statistic: 202.9 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ms"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.049924 -0.015644 -0.002702 0.012486 0.144295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.244e+00 1.904e-01 17.037 < 2e-16 ***
## time_value -1.660e-04 1.051e-05 -15.804 < 2e-16 ***
## RestaurantRestrict 4.054e+00 5.688e-01 7.128 3.77e-12 ***
## time_value:RestaurantRestrict -2.168e-04 3.088e-05 -7.022 7.52e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02442 on 480 degrees of freedom
## Multiple R-squared: 0.4708, Adjusted R-squared: 0.4675
## F-statistic: 142.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "mt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05289 -0.01785 -0.00195 0.01470 0.11772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.745e+00 2.076e-01 22.860 < 2e-16 ***
## time_value -2.455e-04 1.146e-05 -21.430 < 2e-16 ***
## RestaurantRestrict 2.153e+00 6.052e-01 3.558 0.000411 ***
## time_value:RestaurantRestrict -1.129e-04 3.286e-05 -3.436 0.000643 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02638 on 480 degrees of freedom
## Multiple R-squared: 0.5655, Adjusted R-squared: 0.5628
## F-statistic: 208.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058415 -0.014169 -0.001423 0.014318 0.117519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.829e+00 1.907e-01 20.077 < 2e-16 ***
## time_value -1.965e-04 1.053e-05 -18.666 < 2e-16 ***
## RestaurantRestrict 4.294e+00 5.363e-01 8.008 8.91e-15 ***
## time_value:RestaurantRestrict -2.280e-04 2.913e-05 -7.829 3.18e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0239 on 480 degrees of freedom
## Multiple R-squared: 0.5806, Adjusted R-squared: 0.578
## F-statistic: 221.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.094994 -0.019990 0.000978 0.021268 0.140990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.919e+00 1.503e-01 19.426 <2e-16 ***
## time_value -1.452e-04 8.246e-06 -17.610 <2e-16 ***
## RestaurantRestrict 1.183e+01 5.040e+00 2.347 0.0193 *
## time_value:RestaurantRestrict -6.399e-04 2.743e-04 -2.333 0.0201 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03452 on 480 degrees of freedom
## Multiple R-squared: 0.4577, Adjusted R-squared: 0.4543
## F-statistic: 135 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ne"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076064 -0.022140 -0.002974 0.017037 0.155924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.663e+00 1.782e-01 20.55 <2e-16 ***
## time_value -1.867e-04 9.807e-06 -19.04 <2e-16 ***
## RestaurantRestrict 1.267e+01 1.235e+00 10.26 <2e-16 ***
## time_value:RestaurantRestrict -6.841e-04 6.698e-05 -10.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03389 on 480 degrees of freedom
## Multiple R-squared: 0.5283, Adjusted R-squared: 0.5253
## F-statistic: 179.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.108868 -0.020836 -0.000162 0.020462 0.165840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.420e+00 1.789e-01 13.528 <2e-16 ***
## time_value -1.174e-04 9.839e-06 -11.931 <2e-16 ***
## RestaurantRestrict 1.328e+01 1.339e+00 9.918 <2e-16 ***
## time_value:RestaurantRestrict -7.170e-04 7.273e-05 -9.858 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0359 on 480 degrees of freedom
## Multiple R-squared: 0.4795, Adjusted R-squared: 0.4763
## F-statistic: 147.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nj"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083629 -0.021404 -0.005091 0.013902 0.163997
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.966e+00 2.754e-01 18.03 <2e-16 ***
## time_value -2.591e-04 1.520e-05 -17.04 <2e-16 ***
## RestaurantRestrict 1.288e+01 7.654e-01 16.82 <2e-16 ***
## time_value:RestaurantRestrict -6.881e-04 4.157e-05 -16.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03435 on 480 degrees of freedom
## Multiple R-squared: 0.7738, Adjusted R-squared: 0.7724
## F-statistic: 547.4 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nm"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060151 -0.016470 -0.002778 0.012387 0.140710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.966e+00 2.034e-01 24.416 <2e-16 ***
## time_value -2.573e-04 1.123e-05 -22.921 <2e-16 ***
## RestaurantRestrict 8.237e-01 5.858e-01 1.406 0.160
## time_value:RestaurantRestrict -3.902e-05 3.181e-05 -1.227 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02573 on 480 degrees of freedom
## Multiple R-squared: 0.582, Adjusted R-squared: 0.5794
## F-statistic: 222.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "nv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055035 -0.015897 -0.002439 0.014451 0.133383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.091e+00 2.039e-01 24.972 < 2e-16 ***
## time_value -2.639e-04 1.125e-05 -23.455 < 2e-16 ***
## RestaurantRestrict 5.026e+00 5.944e-01 8.455 3.38e-16 ***
## time_value:RestaurantRestrict -2.657e-04 3.227e-05 -8.231 1.76e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02591 on 480 degrees of freedom
## Multiple R-squared: 0.6816, Adjusted R-squared: 0.6796
## F-statistic: 342.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ny"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.103189 -0.020078 -0.003337 0.015194 0.151693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.074e+00 2.450e-01 20.71 <2e-16 ***
## time_value -2.635e-04 1.353e-05 -19.48 <2e-16 ***
## RestaurantRestrict 1.063e+01 6.809e-01 15.62 <2e-16 ***
## time_value:RestaurantRestrict -5.675e-04 3.698e-05 -15.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03056 on 480 degrees of freedom
## Multiple R-squared: 0.7613, Adjusted R-squared: 0.7598
## F-statistic: 510.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "oh"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.084731 -0.019234 -0.003863 0.014510 0.115198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.225e+00 2.210e-01 23.64 <2e-16 ***
## time_value -2.724e-04 1.220e-05 -22.33 <2e-16 ***
## RestaurantRestrict 6.520e+00 6.070e-01 10.74 <2e-16 ***
## time_value:RestaurantRestrict -3.479e-04 3.297e-05 -10.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02743 on 480 degrees of freedom
## Multiple R-squared: 0.6565, Adjusted R-squared: 0.6543
## F-statistic: 305.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ok"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08314 -0.01851 -0.00263 0.01455 0.14100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.205e+00 1.328e-01 16.603 < 2e-16 ***
## time_value -1.076e-04 7.289e-06 -14.765 < 2e-16 ***
## RestaurantRestrict 3.094e+01 4.740e+00 6.526 1.71e-10 ***
## time_value:RestaurantRestrict -1.679e-03 2.579e-04 -6.512 1.87e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03044 on 480 degrees of freedom
## Multiple R-squared: 0.4392, Adjusted R-squared: 0.4357
## F-statistic: 125.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "or"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069125 -0.016077 -0.001762 0.013409 0.126173
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.073e+00 1.947e-01 26.052 < 2e-16 ***
## time_value -2.631e-04 1.075e-05 -24.476 < 2e-16 ***
## RestaurantRestrict 3.181e+00 5.475e-01 5.809 1.14e-08 ***
## time_value:RestaurantRestrict -1.660e-04 2.974e-05 -5.581 4.00e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0244 on 480 degrees of freedom
## Multiple R-squared: 0.6729, Adjusted R-squared: 0.6709
## F-statistic: 329.2 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "pa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070717 -0.019215 -0.004558 0.014963 0.121998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.398e+00 2.343e-01 23.04 <2e-16 ***
## time_value -2.812e-04 1.293e-05 -21.74 <2e-16 ***
## RestaurantRestrict 7.819e+00 6.589e-01 11.87 <2e-16 ***
## time_value:RestaurantRestrict -4.169e-04 3.579e-05 -11.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02936 on 480 degrees of freedom
## Multiple R-squared: 0.6817, Adjusted R-squared: 0.6797
## F-statistic: 342.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ri"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079615 -0.020333 -0.004588 0.014037 0.140250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.442e+00 2.506e-01 21.72 <2e-16 ***
## time_value -2.840e-04 1.383e-05 -20.53 <2e-16 ***
## RestaurantRestrict 7.200e+00 7.046e-01 10.22 <2e-16 ***
## time_value:RestaurantRestrict -3.829e-04 3.827e-05 -10.01 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0314 on 480 degrees of freedom
## Multiple R-squared: 0.6579, Adjusted R-squared: 0.6558
## F-statistic: 307.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sc"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056817 -0.014553 -0.001451 0.013637 0.111562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.644e+00 1.473e-01 17.944 < 2e-16 ***
## time_value -1.316e-04 8.118e-06 -16.209 < 2e-16 ***
## RestaurantRestrict 5.707e+00 6.878e-01 8.297 1.08e-15 ***
## time_value:RestaurantRestrict -3.068e-04 3.733e-05 -8.220 1.92e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02518 on 480 degrees of freedom
## Multiple R-squared: 0.4712, Adjusted R-squared: 0.4679
## F-statistic: 142.6 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "sd"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088113 -0.021948 -0.001727 0.016525 0.221611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.908e+00 1.580e-01 18.408 <2e-16 ***
## time_value -1.445e-04 8.666e-06 -16.681 <2e-16 ***
## RestaurantRestrict 4.222e+01 2.407e+01 1.754 0.0800 .
## time_value:RestaurantRestrict -2.295e-03 1.310e-03 -1.751 0.0805 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03697 on 480 degrees of freedom
## Multiple R-squared: 0.3955, Adjusted R-squared: 0.3917
## F-statistic: 104.7 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tn"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073330 -0.018066 0.000501 0.018542 0.116409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.966e+00 1.175e-01 16.741 < 2e-16 ***
## time_value -9.370e-05 6.447e-06 -14.534 < 2e-16 ***
## RestaurantRestrict 1.957e+01 4.081e+00 4.795 2.18e-06 ***
## time_value:RestaurantRestrict -1.061e-03 2.221e-04 -4.777 2.37e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.027 on 480 degrees of freedom
## Multiple R-squared: 0.4794, Adjusted R-squared: 0.4762
## F-statistic: 147.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "tx"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059293 -0.015903 -0.002166 0.010722 0.114169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.106e+00 1.989e-01 20.64 <2e-16 ***
## time_value -2.119e-04 1.098e-05 -19.31 <2e-16 ***
## RestaurantRestrict 6.110e+00 5.799e-01 10.54 <2e-16 ***
## time_value:RestaurantRestrict -3.255e-04 3.149e-05 -10.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02527 on 480 degrees of freedom
## Multiple R-squared: 0.6399, Adjusted R-squared: 0.6376
## F-statistic: 284.3 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "ut"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051911 -0.015272 -0.002355 0.010377 0.120552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.922e+00 1.981e-01 24.851 < 2e-16 ***
## time_value -2.564e-04 1.093e-05 -23.459 < 2e-16 ***
## RestaurantRestrict 4.624e+00 5.705e-01 8.106 4.39e-15 ***
## time_value:RestaurantRestrict -2.450e-04 3.098e-05 -7.909 1.80e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02505 on 480 degrees of freedom
## Multiple R-squared: 0.6497, Adjusted R-squared: 0.6475
## F-statistic: 296.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "va"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.069167 -0.017105 -0.003479 0.013861 0.138584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.528e+00 2.079e-01 21.777 <2e-16 ***
## time_value -2.345e-04 1.148e-05 -20.433 <2e-16 ***
## RestaurantRestrict 5.297e+00 5.847e-01 9.060 <2e-16 ***
## time_value:RestaurantRestrict -2.802e-04 3.176e-05 -8.822 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02606 on 480 degrees of freedom
## Multiple R-squared: 0.692, Adjusted R-squared: 0.6901
## F-statistic: 359.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "vt"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.085054 -0.021188 -0.003169 0.018239 0.115283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.621e+00 2.544e-01 26.026 < 2e-16 ***
## time_value -3.478e-04 1.404e-05 -24.771 < 2e-16 ***
## RestaurantRestrict 2.393e+00 7.153e-01 3.345 0.000888 ***
## time_value:RestaurantRestrict -1.237e-04 3.885e-05 -3.184 0.001546 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03188 on 480 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6198
## F-statistic: 263.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wa"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.063874 -0.018834 -0.003506 0.015372 0.174236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.605e+00 2.329e-01 19.767 < 2e-16 ***
## time_value -2.377e-04 1.286e-05 -18.489 < 2e-16 ***
## RestaurantRestrict 4.480e+00 6.473e-01 6.922 1.44e-11 ***
## time_value:RestaurantRestrict -2.356e-04 3.516e-05 -6.700 5.85e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02905 on 480 degrees of freedom
## Multiple R-squared: 0.654, Adjusted R-squared: 0.6519
## F-statistic: 302.5 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wi"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.099428 -0.023029 -0.002697 0.022294 0.198592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.437e+00 1.647e-01 14.802 <2e-16 ***
## time_value -1.177e-04 9.036e-06 -13.028 <2e-16 ***
## RestaurantRestrict -2.336e+00 6.343e+00 -0.368 0.713
## time_value:RestaurantRestrict 1.326e-04 3.454e-04 0.384 0.701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03802 on 480 degrees of freedom
## Multiple R-squared: 0.4219, Adjusted R-squared: 0.4183
## F-statistic: 116.8 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wv"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.066171 -0.018287 -0.004128 0.015322 0.118210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.258e+00 2.126e-01 29.435 < 2e-16 ***
## time_value -3.290e-04 1.173e-05 -28.037 < 2e-16 ***
## RestaurantRestrict 2.128e+00 6.050e-01 3.516 0.000479 ***
## time_value:RestaurantRestrict -1.102e-04 3.286e-05 -3.355 0.000856 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02677 on 480 degrees of freedom
## Multiple R-squared: 0.6851, Adjusted R-squared: 0.6831
## F-statistic: 348.1 on 3 and 480 DF, p-value: < 2.2e-16
##
## [1] "wy"
## [1] "---------------------"
##
## Call:
## lm(formula = value ~ time_value * RestaurantRestrict, data = ftime.policy.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055959 -0.017552 -0.004304 0.013083 0.124670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.035e+00 2.110e-01 23.866 < 2e-16 ***
## time_value -2.612e-04 1.164e-05 -22.435 < 2e-16 ***
## RestaurantRestrict 3.798e+00 6.076e-01 6.251 9.01e-10 ***
## time_value:RestaurantRestrict -2.023e-04 3.300e-05 -6.130 1.84e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02668 on 480 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6257
## F-statistic: 270.1 on 3 and 480 DF, p-value: < 2.2e-16Non-parametric Regression
Full time work prop
Emergency Declaration
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
ftime.policy.df <- as.data.frame(ftime.policy.df)
ftime.policy.df$time_value <- as.numeric(ftime.policy.df$time_value)
lm.fit <- gam(value~ EmergDec + s(time_value)+ s(time_value,by = EmergDec),data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04680 0.01745 2.683 0.00756 **
## EmergDec -0.01930 0.06222 -0.310 0.75659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.431 7.725 17.935 <2e-16 ***
## s(time_value):EmergDec 3.027 3.224 0.214 0.815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.272 Deviance explained = 28.9%
## GCV = 0.00010002 Scale est. = 9.7545e-05 n = 484
## [1] "al"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08209 0.01474 5.570 4.27e-08 ***
## EmergDec -0.03544 0.02354 -1.506 0.133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.888 8.515 27.340 <2e-16 ***
## s(time_value):EmergDec 1.658 1.690 1.844 0.167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.518 Deviance explained = 52.8%
## GCV = 0.00021886 Scale est. = 0.00021387 n = 484
## [1] "ar"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06789 0.01887 3.598 0.000354 ***
## EmergDec 0.04509 0.77606 0.058 0.953697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 5.899 6.330 41.248 <2e-16 ***
## s(time_value):EmergDec 5.295 5.465 1.566 0.453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.552 Deviance explained = 56.3%
## GCV = 0.00016048 Scale est. = 0.00015627 n = 484
## [1] "az"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.067357 0.007630 8.828 <2e-16 ***
## EmergDec -0.010667 0.008901 -1.198 0.231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.297 8.218 13.76 < 2e-16 ***
## s(time_value):EmergDec 1.500 1.500 20.82 5.74e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.459 Deviance explained = 46.9%
## GCV = 9.7806e-05 Scale est. = 9.5725e-05 n = 484
## [1] "ca"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02014 0.02501 0.805 0.421
## EmergDec 0.01982 0.31946 0.062 0.951
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.419 6.742 19.69 < 2e-16 ***
## s(time_value):EmergDec 5.131 5.352 15.35 2.39e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.478 Deviance explained = 49.1%
## GCV = 7.2591e-05 Scale est. = 7.0634e-05 n = 484
## [1] "co"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07117 0.01547 4.601 5.4e-06 ***
## EmergDec 0.04246 0.29804 0.142 0.887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 5.808 6.267 17.830 <2e-16 ***
## s(time_value):EmergDec 4.860 5.119 1.928 0.0469 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.515 Deviance explained = 52.6%
## GCV = 0.000135 Scale est. = 0.00013161 n = 484
## [1] "ct"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06742 0.02566 2.627 0.0089 **
## EmergDec 0.05831 0.49776 0.117 0.9068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.006 6.409 24.257 < 2e-16 ***
## s(time_value):EmergDec 5.008 5.268 4.353 0.000287 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.599 Deviance explained = 60.9%
## GCV = 0.00023758 Scale est. = 0.00023144 n = 484
## [1] "dc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.077524 0.005899 13.142 <2e-16 ***
## EmergDec 0.031219 0.327514 0.095 0.924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 4.356 5.125 30.321 <2e-16 ***
## s(time_value):EmergDec 4.963 5.225 2.484 0.0169 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.49 Deviance explained = 50%
## GCV = 0.00012468 Scale est. = 0.0001219 n = 484
## [1] "de"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08318 0.01099 7.570 1.97e-13 ***
## EmergDec -0.01362 0.03045 -0.447 0.655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.792 7.462 27.105 <2e-16 ***
## s(time_value):EmergDec 2.503 2.679 0.732 0.579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.59 Deviance explained = 59.8%
## GCV = 0.00014603 Scale est. = 0.00014278 n = 484
## [1] "fl"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03855 0.02160 1.785 0.0749 .
## EmergDec 0.05314 0.78478 0.068 0.9460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.225 6.585 28.34 < 2e-16 ***
## s(time_value):EmergDec 5.400 5.507 10.40 0.000129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.524 Deviance explained = 53.6%
## GCV = 9.1459e-05 Scale est. = 8.8979e-05 n = 484
## [1] "ga"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05364 0.01973 2.719 0.0068 **
## EmergDec -0.01151 0.26446 -0.044 0.9653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.150 6.550 20.604 <2e-16 ***
## s(time_value):EmergDec 4.685 4.916 0.696 0.555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.529 Deviance explained = 54%
## GCV = 0.00013913 Scale est. = 0.00013559 n = 484
## [1] "hi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.023328 0.016855 1.384 0.167
## EmergDec 0.001012 0.216416 0.005 0.996
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.727 6.981 21.966 < 2e-16 ***
## s(time_value):EmergDec 5.265 5.414 7.081 0.00018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.302 Deviance explained = 32%
## GCV = 1.484e-05 Scale est. = 1.4427e-05 n = 484
## [1] "ia"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05873 0.03362 1.747 0.0814 .
## EmergDec 0.08489 0.57673 0.147 0.8830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.169 6.538 40.947 < 2e-16 ***
## s(time_value):EmergDec 5.061 5.311 2.933 0.00566 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.677 Deviance explained = 68.4%
## GCV = 0.00027255 Scale est. = 0.00026538 n = 484
## [1] "id"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.062487 0.016598 3.765 0.000188 ***
## EmergDec -0.004416 0.133356 -0.033 0.973598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.280 6.670 21.739 <2e-16 ***
## s(time_value):EmergDec 4.329 4.503 0.688 0.418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.454 Deviance explained = 46.6%
## GCV = 0.00010303 Scale est. = 0.00010045 n = 484
## [1] "il"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04655 0.04113 1.132 0.258
## EmergDec 0.04252 0.40683 0.105 0.917
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.545 6.863 39.780 < 2e-16 ***
## s(time_value):EmergDec 4.823 5.098 5.325 4.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.685 Deviance explained = 69.3%
## GCV = 0.00021981 Scale est. = 0.00021396 n = 484
## [1] "in"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05767 0.01970 2.927 0.00359 **
## EmergDec 0.08773 0.40442 0.217 0.82835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 5.961 6.354 29.972 < 2e-16 ***
## s(time_value):EmergDec 5.103 5.346 2.854 0.00658 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.565 Deviance explained = 57.6%
## GCV = 0.0001318 Scale est. = 0.00012838 n = 484
## [1] "ks"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05999 0.02249 2.667 0.00791 **
## EmergDec -0.05362 0.09929 -0.540 0.58946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.831 7.186 48.593 <2e-16 ***
## s(time_value):EmergDec 3.563 3.760 0.652 0.675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.639 Deviance explained = 64.7%
## GCV = 0.00019824 Scale est. = 0.00019337 n = 484
## [1] "ky"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03127 0.03777 0.828 0.408
## EmergDec 0.04993 0.42071 0.119 0.906
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.487 6.809 33.168 < 2e-16 ***
## s(time_value):EmergDec 4.959 5.223 5.901 1.03e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.597 Deviance explained = 60.7%
## GCV = 0.00017676 Scale est. = 0.00017204 n = 484
## [1] "la"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06672 0.01286 5.187 3.18e-07 ***
## EmergDec 0.02339 0.29520 0.079 0.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 5.550 6.083 46.391 <2e-16 ***
## s(time_value):EmergDec 4.854 5.110 1.301 0.153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.548 Deviance explained = 55.8%
## GCV = 0.00014202 Scale est. = 0.00013853 n = 484
## [1] "ma"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04392 0.03227 1.361 0.174
## EmergDec 0.04483 0.61054 0.073 0.941
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.281 6.641 16.007 < 2e-16 ***
## s(time_value):EmergDec 5.135 5.366 3.937 0.000656 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.592 Deviance explained = 60.2%
## GCV = 0.00020874 Scale est. = 0.00020317 n = 484
## [1] "md"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0008417 0.0559975 0.015 0.988
## EmergDec 0.0365687 0.6096245 0.060 0.952
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.780 7.026 22.67 < 2e-16 ***
## s(time_value):EmergDec 5.144 5.341 18.34 4.81e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.639 Deviance explained = 64.8%
## GCV = 0.00017091 Scale est. = 0.00016617 n = 484
## [1] "me"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06444 0.02313 2.786 0.00555 **
## EmergDec 0.01449 0.41478 0.035 0.97215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.22 6.606 18.968 <2e-16 ***
## s(time_value):EmergDec 4.92 5.184 1.087 0.247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.496 Deviance explained = 50.8%
## GCV = 0.00015381 Scale est. = 0.0001498 n = 484
## [1] "mi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.041207 0.005991 6.878 1.96e-11 ***
## EmergDec 24.731620 56.477609 0.438 0.662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.955 8.998 39.14 <2e-16 ***
## s(time_value):EmergDec 7.100 7.364 17.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.631 Deviance explained = 64.3%
## GCV = 0.0001311 Scale est. = 0.00012635 n = 484
## [1] "mn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08683 0.01525 5.693 2.19e-08 ***
## EmergDec -0.02732 0.04463 -0.612 0.541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.913 7.512 51.315 <2e-16 ***
## s(time_value):EmergDec 2.603 2.787 0.366 0.804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.68 Deviance explained = 68.6%
## GCV = 0.00023365 Scale est. = 0.00022833 n = 484
## [1] "mo"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07416 0.02273 3.262 0.00118 **
## EmergDec 0.04391 0.56678 0.077 0.93828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.013 6.435 49.112 <2e-16 ***
## s(time_value):EmergDec 5.019 5.277 0.575 0.447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.623 Deviance explained = 63.2%
## GCV = 0.0002156 Scale est. = 0.00021002 n = 484
## [1] "ms"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04994 0.04088 -1.222 0.222
## EmergDec -0.11866 0.28020 -0.423 0.672
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.059 7.284 40.097 < 2e-16 ***
## s(time_value):EmergDec 4.355 4.606 6.496 1.57e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.55 Deviance explained = 56.1%
## GCV = 0.00014998 Scale est. = 0.00014598 n = 484
## [1] "mt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.080916 0.010606 7.629 1.31e-13 ***
## EmergDec -0.006154 0.012743 -0.483 0.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.196 8.764 24.59 < 2e-16 ***
## s(time_value):EmergDec 1.500 1.500 18.41 3.69e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.433 Deviance explained = 44.5%
## GCV = 0.00010463 Scale est. = 0.00010221 n = 484
## [1] "nc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05843 0.02013 2.902 0.00388 **
## EmergDec 0.04608 0.32182 0.143 0.88621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.121 6.505 19.152 <2e-16 ***
## s(time_value):EmergDec 4.941 5.206 2.479 0.0148 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.487 Deviance explained = 50%
## GCV = 0.00011923 Scale est. = 0.00011613 n = 484
## [1] "nd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0434941 0.0348193 1.249 0.212
## EmergDec 0.0004049 0.8559995 0.000 1.000
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.373 6.730 45.409 <2e-16 ***
## s(time_value):EmergDec 5.188 5.403 0.786 0.597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.657 Deviance explained = 66.5%
## GCV = 0.00023903 Scale est. = 0.00023258 n = 484
## [1] "ne"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05279 0.02249 2.348 0.0193 *
## EmergDec -0.07055 0.04844 -1.456 0.1459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.534 8.767 48.261 <2e-16 ***
## s(time_value):EmergDec 1.780 1.863 1.068 0.253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.643 Deviance explained = 65.1%
## GCV = 0.00025429 Scale est. = 0.00024808 n = 484
## [1] "nh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.077475 0.020474 3.784 0.000174 ***
## EmergDec -0.009928 0.224550 -0.044 0.964752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 5.968 6.413 22.12 <2e-16 ***
## s(time_value):EmergDec 4.486 4.665 0.66 0.547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.58 Deviance explained = 59%
## GCV = 0.00022857 Scale est. = 0.00022293 n = 484
## [1] "nj"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03506 0.04461 0.786 0.432
## EmergDec 0.09903 0.78749 0.126 0.900
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.436 6.767 17.67 < 2e-16 ***
## s(time_value):EmergDec 5.210 5.408 13.03 2.89e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.661 Deviance explained = 66.9%
## GCV = 0.00025977 Scale est. = 0.00025271 n = 484
## [1] "nm"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05887 0.01017 5.788 1.3e-08 ***
## EmergDec 0.02336 0.19640 0.119 0.905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 5.808 6.266 21.943 <2e-16 ***
## s(time_value):EmergDec 4.861 5.120 0.731 0.436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.411 Deviance explained = 42.5%
## GCV = 5.8383e-05 Scale est. = 5.6915e-05 n = 484
## [1] "nv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05434 0.01338 4.063 5.68e-05 ***
## EmergDec 0.02726 0.17876 0.152 0.879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.183 6.572 26.85 < 2e-16 ***
## s(time_value):EmergDec 4.747 4.997 3.04 0.00974 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.496 Deviance explained = 50.8%
## GCV = 5.6025e-05 Scale est. = 5.4586e-05 n = 484
## [1] "ny"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02248 0.05645 0.398 0.691
## EmergDec 0.05864 0.55030 0.107 0.915
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.831 7.069 21.51 6.73e-16 ***
## s(time_value):EmergDec 5.024 5.260 11.80 7.00e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.588 Deviance explained = 59.8%
## GCV = 0.00018087 Scale est. = 0.00017588 n = 484
## [1] "oh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03362 0.03572 0.941 0.347
## EmergDec 0.05747 0.42305 0.136 0.892
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.503 6.826 31.532 < 2e-16 ***
## s(time_value):EmergDec 4.971 5.233 4.983 7.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.621 Deviance explained = 63.1%
## GCV = 0.00016339 Scale est. = 0.00015901 n = 484
## [1] "ok"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04989 0.02048 2.435 0.0152 *
## EmergDec -0.01970 0.66585 -0.030 0.9764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.076 6.485 48.494 <2e-16 ***
## s(time_value):EmergDec 5.190 5.407 0.493 0.785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.584 Deviance explained = 59.5%
## GCV = 0.00015169 Scale est. = 0.00014769 n = 484
## [1] "or"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006248 0.027384 0.228 0.820
## EmergDec 0.044593 0.604161 0.074 0.941
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.431 6.757 18.41 < 2e-16 ***
## s(time_value):EmergDec 5.336 5.472 16.42 2.27e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.522 Deviance explained = 53.4%
## GCV = 8.6142e-05 Scale est. = 8.3781e-05 n = 484
## [1] "pa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01075 0.05054 -0.213 0.832
## EmergDec 0.04062 0.55449 0.073 0.942
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.798 7.038 39.08 < 2e-16 ***
## s(time_value):EmergDec 5.116 5.325 17.93 3.1e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.651 Deviance explained = 66%
## GCV = 0.00014301 Scale est. = 0.00013905 n = 484
## [1] "ri"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03669 0.03256 1.127 0.260
## EmergDec 0.07539 0.62798 0.120 0.904
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.364 6.707 23.02 < 2e-16 ***
## s(time_value):EmergDec 5.224 5.418 11.76 6.05e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.59 Deviance explained = 60.1%
## GCV = 0.00016076 Scale est. = 0.00015642 n = 484
## [1] "sc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.058097 0.016428 3.537 0.000445 ***
## EmergDec -0.005434 0.149582 -0.036 0.971038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.219 6.615 24.092 <2e-16 ***
## s(time_value):EmergDec 4.422 4.604 0.439 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.478 Deviance explained = 49%
## GCV = 0.0001023 Scale est. = 9.9733e-05 n = 484
## [1] "sd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06917 0.02164 3.197 0.00148 **
## EmergDec -0.03335 0.18121 -0.184 0.85407
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.209 6.612 64.654 <2e-16 ***
## s(time_value):EmergDec 4.349 4.522 0.137 0.973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.62 Deviance explained = 62.9%
## GCV = 0.00019278 Scale est. = 0.00018797 n = 484
## [1] "tn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06542 0.01557 4.203 3.16e-05 ***
## EmergDec 0.00908 0.28435 0.032 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 5.828 6.290 36.69 <2e-16 ***
## s(time_value):EmergDec 4.792 5.039 0.39 0.784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.558 Deviance explained = 56.8%
## GCV = 0.00014038 Scale est. = 0.00013686 n = 484
## [1] "tx"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.014 1.605 -3.125 0.00189 **
## EmergDec -28.635 20.071 -1.427 0.15435
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.893 8.958 47.556 < 2e-16 ***
## s(time_value):EmergDec 6.368 6.988 9.016 1.71e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.661 Deviance explained = 67.2%
## GCV = 0.0001308 Scale est. = 0.00012627 n = 484
## [1] "ut"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1464 0.2348 -0.623 0.533
## EmergDec -0.0132 2.0877 -0.006 0.995
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.484 7.647 10.99 5.45e-12 ***
## s(time_value):EmergDec 5.273 5.383 11.54 3.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.458 Deviance explained = 47.3%
## GCV = 0.0001331 Scale est. = 0.00012918 n = 484
## [1] "va"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06020 0.02092 2.877 0.00419 **
## EmergDec 0.00404 0.29640 0.014 0.98913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.175 6.565 25.144 <2e-16 ***
## s(time_value):EmergDec 4.786 5.041 2.282 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.617 Deviance explained = 62.6%
## GCV = 0.00013677 Scale est. = 0.00013325 n = 484
## [1] "vt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06240 0.01912 3.263 0.00118 **
## EmergDec 0.02055 0.31229 0.066 0.94757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.135 6.537 40.442 <2e-16 ***
## s(time_value):EmergDec 4.813 5.068 2.334 0.025 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.593 Deviance explained = 60.3%
## GCV = 0.00012743 Scale est. = 0.00012415 n = 484
## [1] "wa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02745 0.04432 -0.619 0.536
## EmergDec -0.01477 0.52439 -0.028 0.978
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.667 6.937 25.20 < 2e-16 ***
## s(time_value):EmergDec 5.285 5.419 25.38 7.74e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.609 Deviance explained = 61.9%
## GCV = 9.3058e-05 Scale est. = 9.0471e-05 n = 484
## [1] "wi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.061945 0.025916 2.39 0.0172 *
## EmergDec 0.003616 0.355963 0.01 0.9919
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.179 6.568 67.72 <2e-16 ***
## s(time_value):EmergDec 4.765 5.018 2.34 0.0292 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.706 Deviance explained = 71.3%
## GCV = 0.0002101 Scale est. = 0.0002047 n = 484
## [1] "wv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02200 0.02479 0.887 0.375
## EmergDec -0.02823 0.30398 -0.093 0.926
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.466 6.817 47.379 < 2e-16 ***
## s(time_value):EmergDec 4.718 4.968 4.032 0.000823 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.605 Deviance explained = 61.4%
## GCV = 0.00012943 Scale est. = 0.00012604 n = 484
## [1] "wy"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ EmergDec + s(time_value) + s(time_value, by = EmergDec)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04752 0.01273 3.734 0.000212 ***
## EmergDec -0.04088 0.01578 -2.591 0.009871 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.808 8.983 30.08 < 2e-16 ***
## s(time_value):EmergDec 1.501 1.501 10.71 0.000137 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.49 Deviance explained = 50.1%
## GCV = 9.9461e-05 Scale est. = 9.7034e-05 n = 484School Closure
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
ftime.policy.df <- as.data.frame(ftime.policy.df)
ftime.policy.df$time_value <- as.numeric(ftime.policy.df$time_value)
lm.fit <- gam(value~ SchoolClose + s(time_value)+ s(time_value,by = SchoolClose),data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.055662 0.001106 50.329 <2e-16 ***
## SchoolClose 0.043836 0.181531 0.241 0.809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.614 8.931 16.50 < 2e-16 ***
## s(time_value):SchoolClose 3.625 3.922 4.07 0.00245 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.267 Deviance explained = 28.6%
## GCV = 0.00010111 Scale est. = 9.8238e-05 n = 484
## [1] "al"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.070492 0.000939 75.075 <2e-16 ***
## SchoolClose -0.056159 0.006257 -8.975 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.628 8.96 33.48 < 2e-16 ***
## s(time_value):SchoolClose 1.500 1.50 19.43 4.12e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.515 Deviance explained = 52.6%
## GCV = 0.00022034 Scale est. = 0.00021505 n = 484
## [1] "ar"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.069955 0.001036 67.56 < 2e-16 ***
## SchoolClose -0.032233 0.004410 -7.31 1.15e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.435 8.917 31.50 < 2e-16 ***
## s(time_value):SchoolClose 1.500 1.500 18.56 7.27e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.547 Deviance explained = 55.7%
## GCV = 0.00016174 Scale est. = 0.00015792 n = 484
## [1] "az"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0574657 0.0007841 73.292 < 2e-16 ***
## SchoolClose -0.0221837 0.0033493 -6.623 9.56e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.057 8.773 17.104 < 2e-16 ***
## s(time_value):SchoolClose 1.500 1.500 9.925 0.000233 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.454 Deviance explained = 46.6%
## GCV = 9.8715e-05 Scale est. = 9.646e-05 n = 484
## [1] "ca"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.055841 0.015031 3.715 0.000227 ***
## SchoolClose 0.002096 0.195934 0.011 0.991468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.206 6.597 20.829 <2e-16 ***
## s(time_value):SchoolClose 4.690 4.925 2.162 0.0402 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.476 Deviance explained = 48.8%
## GCV = 7.2753e-05 Scale est. = 7.089e-05 n = 484
## [1] "co"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.063472 0.001078 58.892 <2e-16 ***
## SchoolClose 0.023729 0.144949 0.164 0.87
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.909 8.995 25.51 < 2e-16 ***
## s(time_value):SchoolClose 3.163 3.480 15.86 9.84e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.5 Deviance explained = 51.3%
## GCV = 0.00013969 Scale est. = 0.00013577 n = 484
## [1] "ct"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.071397 0.001637 43.612 <2e-16 ***
## SchoolClose -0.007749 0.076320 -0.102 0.919
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.851 8.975 38.92 <2e-16 ***
## s(time_value):SchoolClose 2.600 3.024 27.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.594 Deviance explained = 60.4%
## GCV = 0.00024079 Scale est. = 0.00023435 n = 484
## [1] "dc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.073425 0.002304 31.866 < 2e-16 ***
## SchoolClose -0.016741 0.002877 -5.819 1.08e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 2.991 3.887 36.82 < 2e-16 ***
## s(time_value):SchoolClose 1.500 1.500 95.07 4.69e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.482 Deviance explained = 48.7%
## GCV = 0.00012529 Scale est. = 0.00012374 n = 484
## [1] "de"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08318 0.01099 7.570 1.97e-13 ***
## SchoolClose -0.01362 0.03045 -0.447 0.655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.792 7.462 27.105 <2e-16 ***
## s(time_value):SchoolClose 2.503 2.679 0.732 0.579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.59 Deviance explained = 59.8%
## GCV = 0.00014603 Scale est. = 0.00014278 n = 484
## [1] "fl"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0593295 0.0009868 60.123 <2e-16 ***
## SchoolClose -0.0073081 0.2734093 -0.027 0.979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.912 8.544 23.61 < 2e-16 ***
## s(time_value):SchoolClose 4.048 4.361 12.39 3.78e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.512 Deviance explained = 52.4%
## GCV = 9.384e-05 Scale est. = 9.123e-05 n = 484
## [1] "ga"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.061419 0.001029 59.670 <2e-16 ***
## SchoolClose 0.014954 0.216245 0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.800 8.984 26.10 < 2e-16 ***
## s(time_value):SchoolClose 3.401 3.652 16.89 6.35e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.521 Deviance explained = 53.4%
## GCV = 0.00014175 Scale est. = 0.00013774 n = 484
## [1] "hi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0313676 0.0007066 44.394 <2e-16 ***
## SchoolClose 0.0048868 0.0266092 0.184 0.854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.832 8.328 22.712 < 2e-16 ***
## s(time_value):SchoolClose 3.871 4.226 5.282 0.000314 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.311 Deviance explained = 32.8%
## GCV = 1.4647e-05 Scale est. = 1.4248e-05 n = 484
## [1] "ia"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03398 0.05116 -0.664 0.507
## SchoolClose -0.15788 0.25653 -0.615 0.539
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.528 7.708 72.530 < 2e-16 ***
## s(time_value):SchoolClose 3.714 3.936 6.719 0.000192 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.685 Deviance explained = 69.3%
## GCV = 0.00026512 Scale est. = 0.00025814 n = 484
## [1] "id"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0611558 0.0007818 78.224 <2e-16 ***
## SchoolClose 0.0260375 0.1694014 0.154 0.878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.933 8.998 26.52 < 2e-16 ***
## s(time_value):SchoolClose 3.195 3.474 18.43 1.82e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.443 Deviance explained = 45.7%
## GCV = 0.0001054 Scale est. = 0.00010243 n = 484
## [1] "il"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0720234 0.0009465 76.093 < 2e-16 ***
## SchoolClose -0.0651657 0.0113041 -5.765 1.48e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.709 8.974 79.59 < 2e-16 ***
## s(time_value):SchoolClose 1.538 1.574 40.50 4.37e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.688 Deviance explained = 69.5%
## GCV = 0.00021739 Scale est. = 0.00021212 n = 484
## [1] "in"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.061761 0.000751 82.236 <2e-16 ***
## SchoolClose -0.041978 0.004846 -8.663 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.772 8.984 38.34 < 2e-16 ***
## s(time_value):SchoolClose 1.500 1.500 20.05 2.76e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.562 Deviance explained = 57.1%
## GCV = 0.00013271 Scale est. = 0.00012948 n = 484
## [1] "ks"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0702767 0.0007526 93.374 <2e-16 ***
## SchoolClose -0.0461976 0.1170548 -0.395 0.693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.877 8.995 69.26 < 2e-16 ***
## s(time_value):SchoolClose 2.086 2.360 11.95 1.94e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.633 Deviance explained = 64.1%
## GCV = 0.0002018 Scale est. = 0.0001966 n = 484
## [1] "ky"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06291 0.01789 3.517 0.000478 ***
## SchoolClose -0.04747 0.03739 -1.270 0.204787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.468 8.749 32.89 <2e-16 ***
## s(time_value):SchoolClose 1.764 1.838 0.57 0.454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.585 Deviance explained = 59.4%
## GCV = 0.0001815 Scale est. = 0.0001771 n = 484
## [1] "la"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0666004 0.0007953 83.741 <2e-16 ***
## SchoolClose -0.2267497 1.6137527 -0.141 0.888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.694 8.972 43.30 < 2e-16 ***
## s(time_value):SchoolClose 4.155 4.439 13.78 2.08e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.557 Deviance explained = 56.9%
## GCV = 0.0001401 Scale est. = 0.00013595 n = 484
## [1] "ma"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.062294 0.003053 20.401 <2e-16 ***
## SchoolClose 0.035751 0.208266 0.172 0.864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 36.05 < 2e-16 ***
## s(time_value):SchoolClose 3.877 4.279 17.98 2.52e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.594 Deviance explained = 60.5%
## GCV = 0.00020852 Scale est. = 0.00020233 n = 484
## [1] "md"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.068279 0.001361 50.171 <2e-16 ***
## SchoolClose 0.012155 0.186761 0.065 0.948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.846 8.987 52.28 <2e-16 ***
## s(time_value):SchoolClose 3.404 3.691 26.23 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.64 Deviance explained = 64.9%
## GCV = 0.00017056 Scale est. = 0.00016571 n = 484
## [1] "me"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.059052 0.001286 45.936 <2e-16 ***
## SchoolClose 0.007651 0.114322 0.067 0.947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 28.14 < 2e-16 ***
## s(time_value):SchoolClose 2.913 3.301 13.17 1.28e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.467 Deviance explained = 48.1%
## GCV = 0.00016305 Scale est. = 0.00015853 n = 484
## [1] "mi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0648328 0.0008204 79.022 <2e-16 ***
## SchoolClose 0.0038921 0.3572017 0.011 0.991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.874 8.995 51.99 <2e-16 ***
## s(time_value):SchoolClose 3.454 3.653 24.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.615 Deviance explained = 62.5%
## GCV = 0.00013545 Scale est. = 0.00013158 n = 484
## [1] "mn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.071900 0.001365 52.659 <2e-16 ***
## SchoolClose -0.044842 0.026637 -1.683 0.093 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.782 8.955 69.14 < 2e-16 ***
## s(time_value):SchoolClose 1.802 2.015 30.74 3.88e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.68 Deviance explained = 68.8%
## GCV = 0.00023376 Scale est. = 0.00022792 n = 484
## [1] "mo"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.071847 0.001221 58.82 <2e-16 ***
## SchoolClose 0.036714 0.366530 0.10 0.92
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.811 8.986 46.04 < 2e-16 ***
## s(time_value):SchoolClose 3.464 3.695 17.10 4.28e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.602 Deviance explained = 61.2%
## GCV = 0.00022815 Scale est. = 0.00022165 n = 484
## [1] "ms"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0657108 0.0006788 96.801 < 2e-16 ***
## SchoolClose -0.0357258 0.0067142 -5.321 1.6e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.664 8.967 37.960 < 2e-16 ***
## s(time_value):SchoolClose 1.500 1.500 8.779 0.000512 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.51 Deviance explained = 52.1%
## GCV = 0.00016265 Scale est. = 0.00015873 n = 484
## [1] "mt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0659608 0.0005032 131.074 <2e-16 ***
## SchoolClose -0.0237969 0.2031040 -0.117 0.907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.884 8.996 37.237 <2e-16 ***
## s(time_value):SchoolClose 2.200 2.439 3.823 0.135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.443 Deviance explained = 45.6%
## GCV = 0.00010309 Scale est. = 0.00010041 n = 484
## [1] "nc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05135 0.01740 2.951 0.00333 **
## SchoolClose -0.01406 0.18747 -0.075 0.94024
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.190 6.596 21.739 <2e-16 ***
## s(time_value):SchoolClose 4.509 4.699 0.576 0.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.488 Deviance explained = 50%
## GCV = 0.00011896 Scale est. = 0.00011597 n = 484
## [1] "nd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.072320 0.001369 52.823 <2e-16 ***
## SchoolClose 0.004593 0.148776 0.031 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.808 8.983 68.78 < 2e-16 ***
## s(time_value):SchoolClose 3.049 3.385 17.51 1.32e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.657 Deviance explained = 66.6%
## GCV = 0.00023889 Scale est. = 0.0002323 n = 484
## [1] "ne"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.069497 0.001343 51.748 <2e-16 ***
## SchoolClose 0.018739 0.163713 0.114 0.909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.00 9.000 54.97 < 2e-16 ***
## s(time_value):SchoolClose 2.85 3.197 12.97 2.19e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.629 Deviance explained = 63.8%
## GCV = 0.00026541 Scale est. = 0.00025809 n = 484
## [1] "nh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.072974 0.001245 58.634 <2e-16 ***
## SchoolClose 0.042182 0.245611 0.172 0.864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.782 8.982 40.78 < 2e-16 ***
## s(time_value):SchoolClose 3.310 3.566 14.69 2.26e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.584 Deviance explained = 59.5%
## GCV = 0.00022746 Scale est. = 0.00022107 n = 484
## [1] "nj"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065840 0.003093 21.284 <2e-16 ***
## SchoolClose 0.042349 0.239678 0.177 0.86
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 47.06 <2e-16 ***
## s(time_value):SchoolClose 3.828 4.222 24.22 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.66 Deviance explained = 66.9%
## GCV = 0.00026147 Scale est. = 0.00025373 n = 484
## [1] "nm"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05267 0.01052 5.007 7.83e-07 ***
## SchoolClose -0.01777 0.02230 -0.797 0.426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.571 8.799 19.012 <2e-16 ***
## s(time_value):SchoolClose 1.746 1.824 0.222 0.717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.415 Deviance explained = 42.8%
## GCV = 5.7935e-05 Scale est. = 5.6521e-05 n = 484
## [1] "nv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.052611 0.000434 121.214 <2e-16 ***
## SchoolClose -0.121057 0.897054 -0.135 0.893
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.865 8.994 35.25 < 2e-16 ***
## s(time_value):SchoolClose 3.682 3.888 13.12 1.84e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.507 Deviance explained = 52.1%
## GCV = 5.5005e-05 Scale est. = 5.3408e-05 n = 484
## [1] "ny"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065052 0.002114 30.765 <2e-16 ***
## SchoolClose 0.035808 0.117324 0.305 0.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 35.51 < 2e-16 ***
## s(time_value):SchoolClose 3.232 3.677 18.82 5.83e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.588 Deviance explained = 59.9%
## GCV = 0.0001807 Scale est. = 0.00017558 n = 484
## [1] "oh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0677013 0.0008586 78.85 <2e-16 ***
## SchoolClose -0.0556637 0.0052567 -10.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.84 8.992 53.95 < 2e-16 ***
## s(time_value):SchoolClose 1.50 1.500 26.61 4.04e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.623 Deviance explained = 63.1%
## GCV = 0.00016241 Scale est. = 0.00015843 n = 484
## [1] "ok"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.062879 0.001462 43.006 <2e-16 ***
## SchoolClose 0.014000 0.125930 0.111 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.919 8.993 41.3 < 2e-16 ***
## s(time_value):SchoolClose 3.236 3.590 11.5 4.95e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.579 Deviance explained = 59%
## GCV = 0.00015379 Scale est. = 0.00014945 n = 484
## [1] "or"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0580282 0.0006716 86.405 <2e-16 ***
## SchoolClose -0.0232835 0.0875674 -0.266 0.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.946 8.999 38.00 < 2e-16 ***
## s(time_value):SchoolClose 2.814 3.149 23.15 1.92e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.515 Deviance explained = 52.7%
## GCV = 8.7413e-05 Scale est. = 8.5018e-05 n = 484
## [1] "pa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0682882 0.0008601 79.394 <2e-16 ***
## SchoolClose -0.0181261 0.1857988 -0.098 0.922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.929 8.998 60.95 <2e-16 ***
## s(time_value):SchoolClose 3.144 3.430 31.19 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.652 Deviance explained = 66.1%
## GCV = 0.00014276 Scale est. = 0.00013875 n = 484
## [1] "ri"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065970 0.003278 20.127 <2e-16 ***
## SchoolClose 0.005125 0.200603 0.026 0.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.368 8.697 39.63 < 2e-16 ***
## s(time_value):SchoolClose 4.351 4.579 10.58 9.79e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.603 Deviance explained = 61.4%
## GCV = 0.00015616 Scale est. = 0.00015157 n = 484
## [1] "sc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0595294 0.0006846 86.960 <2e-16 ***
## SchoolClose 0.0145132 0.3269380 0.044 0.965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.845 8.992 29.62 < 2e-16 ***
## s(time_value):SchoolClose 3.432 3.626 17.55 2.45e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.486 Deviance explained = 49.9%
## GCV = 0.00010109 Scale est. = 9.8217e-05 n = 484
## [1] "sd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.069311 0.000676 102.531 < 2e-16 ***
## SchoolClose -0.032470 0.012142 -2.674 0.00775 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.821 8.99 75.91 < 2e-16 ***
## s(time_value):SchoolClose 1.500 1.50 17.02 2.19e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.61 Deviance explained = 61.9%
## GCV = 0.00019766 Scale est. = 0.00019284 n = 484
## [1] "tn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065232 0.001215 53.674 <2e-16 ***
## SchoolClose -0.009510 0.057325 -0.166 0.868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.496 8.857 31.40 < 2e-16 ***
## s(time_value):SchoolClose 2.649 3.042 10.91 4.4e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.556 Deviance explained = 56.7%
## GCV = 0.00014103 Scale est. = 0.00013735 n = 484
## [1] "tx"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0604060 0.0010701 56.450 <2e-16 ***
## SchoolClose 0.0008176 0.1196646 0.007 0.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.491 8.892 43.57 < 2e-16 ***
## s(time_value):SchoolClose 3.032 3.367 11.54 9.48e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.603 Deviance explained = 61.3%
## GCV = 0.00015192 Scale est. = 0.00014783 n = 484
## [1] "ut"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0627602 0.0183175 3.426 0.000666 ***
## SchoolClose -0.0009915 0.2012790 -0.005 0.996072
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.165 6.576 10.691 9.05e-12 ***
## s(time_value):SchoolClose 4.515 4.705 0.441 0.734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.441 Deviance explained = 45.4%
## GCV = 0.0001368 Scale est. = 0.00013336 n = 484
## [1] "va"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0659211 0.0008929 73.830 <2e-16 ***
## SchoolClose 0.0257400 0.2077746 0.124 0.901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.871 8.994 48.82 < 2e-16 ***
## s(time_value):SchoolClose 3.279 3.532 20.26 7.08e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.615 Deviance explained = 62.5%
## GCV = 0.00013777 Scale est. = 0.00013388 n = 484
## [1] "vt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.068409 0.001817 37.647 <2e-16 ***
## SchoolClose 0.001954 0.035123 0.056 0.956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.655 8.837 42.184 < 2e-16 ***
## s(time_value):SchoolClose 2.537 3.017 8.838 9.2e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.592 Deviance explained = 60.2%
## GCV = 0.00012789 Scale est. = 0.00012454 n = 484
## [1] "wa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.056869 0.001429 39.798 <2e-16 ***
## SchoolClose 0.039114 0.273746 0.143 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.862 8.988 37.16 <2e-16 ***
## s(time_value):SchoolClose 4.108 4.423 21.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.589 Deviance explained = 60%
## GCV = 9.8005e-05 Scale est. = 9.5075e-05 n = 484
## [1] "wi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0756394 0.0009111 83.023 <2e-16 ***
## SchoolClose -0.0532715 0.1043222 -0.511 0.61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.863 8.993 90.41 < 2e-16 ***
## s(time_value):SchoolClose 2.433 2.735 27.76 7.94e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.702 Deviance explained = 70.9%
## GCV = 0.00021373 Scale est. = 0.00020807 n = 484
## [1] "wv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05516 0.01943 2.839 0.00473 **
## SchoolClose -0.01974 0.18271 -0.108 0.91399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.253 6.642 48.276 <2e-16 ***
## s(time_value):SchoolClose 4.458 4.648 1.469 0.11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.6 Deviance explained = 60.9%
## GCV = 0.0001309 Scale est. = 0.0001276 n = 484
## [1] "wy"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ SchoolClose + s(time_value) + s(time_value, by = SchoolClose)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.058793 0.000582 101.020 < 2e-16 ***
## SchoolClose -0.032927 0.004723 -6.971 1.06e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.97 9.0 36.37 < 2e-16 ***
## s(time_value):SchoolClose 1.50 1.5 12.50 4.05e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.484 Deviance explained = 49.5%
## GCV = 0.00010068 Scale est. = 9.819e-05 n = 484Bar Restriction
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
ftime.policy.df <- as.data.frame(ftime.policy.df)
ftime.policy.df$time_value <- as.numeric(ftime.policy.df$time_value)
lm.fit <- gam(value~ BarRestrict + s(time_value)+ s(time_value,by = BarRestrict),data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0558057 0.0005208 107.162 < 2e-16 ***
## BarRestrict -0.0210209 0.0057426 -3.661 0.00028 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.881 8.996 18.151 < 2e-16 ***
## s(time_value):BarRestrict 1.500 1.500 5.707 0.00444 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.267 Deviance explained = 28.4%
## GCV = 0.00010067 Scale est. = 9.8203e-05 n = 484
## [1] "al"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03498 0.03501 -0.999 0.318
## BarRestrict -0.16035 0.13461 -1.191 0.234
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.319 8.438 28.09 < 2e-16 ***
## s(time_value):BarRestrict 2.658 2.907 3.77 0.00843 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.523 Deviance explained = 53.4%
## GCV = 0.00021732 Scale est. = 0.00021172 n = 484
## [1] "ar"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01223 0.02417 -0.506 0.613
## BarRestrict -0.12493 0.07676 -1.627 0.104
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.667 8.759 31.498 <2e-16 ***
## s(time_value):BarRestrict 2.089 2.315 3.938 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.547 Deviance explained = 55.8%
## GCV = 0.00016191 Scale est. = 0.00015781 n = 484
## [1] "az"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02833 0.04119 -0.688 0.492
## BarRestrict -0.11168 0.24914 -0.448 0.654
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.845 7.941 19.36 < 2e-16 ***
## s(time_value):BarRestrict 3.704 3.885 11.19 3.91e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.464 Deviance explained = 47.7%
## GCV = 9.7476e-05 Scale est. = 9.4848e-05 n = 484
## [1] "ca"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03594 0.02041 1.761 0.0789 .
## BarRestrict -0.02539 0.19189 -0.132 0.8948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.640 6.959 20.94 <2e-16 ***
## s(time_value):BarRestrict 4.555 4.789 2.33 0.0231 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.486 Deviance explained = 49.9%
## GCV = 7.1385e-05 Scale est. = 6.9513e-05 n = 484
## [1] "co"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006166 0.031562 0.195 0.845
## BarRestrict -0.066743 0.257869 -0.259 0.796
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.866 7.134 19.278 < 2e-16 ***
## s(time_value):BarRestrict 4.442 4.670 3.567 0.00257 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.531 Deviance explained = 54.3%
## GCV = 0.00013074 Scale est. = 0.00012728 n = 484
## [1] "ct"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06736 0.02025 3.327 0.000947 ***
## BarRestrict -0.05636 0.04610 -1.222 0.222156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.234 8.581 20.700 <2e-16 ***
## s(time_value):BarRestrict 1.905 1.999 0.756 0.464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.587 Deviance explained = 59.6%
## GCV = 0.00024418 Scale est. = 0.00023831 n = 484
## [1] "dc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.073425 0.002304 31.866 < 2e-16 ***
## BarRestrict -0.016741 0.002877 -5.819 1.08e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 2.991 3.887 36.82 < 2e-16 ***
## s(time_value):BarRestrict 1.500 1.500 95.07 4.69e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.482 Deviance explained = 48.7%
## GCV = 0.00012529 Scale est. = 0.00012374 n = 484
## [1] "de"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07416 0.01126 6.583 1.23e-10 ***
## BarRestrict -0.01967 0.04507 -0.436 0.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.234 6.837 32.599 <2e-16 ***
## s(time_value):BarRestrict 3.243 3.494 0.187 0.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.585 Deviance explained = 59.4%
## GCV = 0.00014782 Scale est. = 0.00014447 n = 484
## [1] "fl"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.060754 0.001507 40.314 <2e-16 ***
## BarRestrict -0.005974 0.168376 -0.035 0.972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.804 7.637 21.72 < 2e-16 ***
## s(time_value):BarRestrict 4.505 4.683 7.18 2.1e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.504 Deviance explained = 51.6%
## GCV = 9.519e-05 Scale est. = 9.2671e-05 n = 484
## [1] "ga"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.09147 0.06501 -1.407 0.160
## BarRestrict -0.12385 0.99932 -0.124 0.901
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.107 7.245 28.20 < 2e-16 ***
## s(time_value):BarRestrict 5.134 5.348 12.46 1.17e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.535 Deviance explained = 54.7%
## GCV = 0.0001377 Scale est. = 0.00013379 n = 484
## [1] "hi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.033960 0.002507 13.544 <2e-16 ***
## BarRestrict -0.003922 0.003276 -1.197 0.232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.873 7.894 20.06 < 2e-16 ***
## s(time_value):BarRestrict 1.512 1.515 10.64 0.00127 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.298 Deviance explained = 31.1%
## GCV = 1.482e-05 Scale est. = 1.4518e-05 n = 484
## [1] "ia"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01479 0.02465 0.600 0.5488
## BarRestrict -0.13579 0.05192 -2.615 0.0092 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.852 8.939 50.308 <2e-16 ***
## s(time_value):BarRestrict 1.647 1.730 4.105 0.0128 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.666 Deviance explained = 67.4%
## GCV = 0.00028065 Scale est. = 0.00027369 n = 484
## [1] "id"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04658 0.05859 -0.795 0.427
## BarRestrict 0.05546 1.25528 0.044 0.965
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.101 7.25 28.89 < 2e-16 ***
## s(time_value):BarRestrict 5.300 5.45 12.07 1.04e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.477 Deviance explained = 49.1%
## GCV = 9.9037e-05 Scale est. = 9.6193e-05 n = 484
## [1] "il"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03831 0.02857 1.341 0.181
## BarRestrict -0.04652 0.60799 -0.077 0.939
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.29 6.670 51.889 <2e-16 ***
## s(time_value):BarRestrict 5.02 5.279 1.607 0.126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.693 Deviance explained = 70%
## GCV = 0.00021463 Scale est. = 0.00020895 n = 484
## [1] "in"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02705 0.02667 1.014 0.311
## BarRestrict -0.04771 0.18315 -0.261 0.795
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.798 7.104 33.637 <2e-16 ***
## s(time_value):BarRestrict 4.236 4.434 1.524 0.152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.573 Deviance explained = 58.3%
## GCV = 0.00012944 Scale est. = 0.00012609 n = 484
## [1] "ks"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.068501 0.000742 92.318 < 2e-16 ***
## BarRestrict -0.044873 0.010462 -4.289 2.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.483 8.925 60.188 < 2e-16 ***
## s(time_value):BarRestrict 1.500 1.500 6.771 0.00208 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.593 Deviance explained = 60.2%
## GCV = 0.00022294 Scale est. = 0.00021765 n = 484
## [1] "ky"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06291 0.01789 3.517 0.000478 ***
## BarRestrict -0.04747 0.03739 -1.270 0.204787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.468 8.749 32.89 <2e-16 ***
## s(time_value):BarRestrict 1.764 1.838 0.57 0.454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.585 Deviance explained = 59.4%
## GCV = 0.0001815 Scale est. = 0.0001771 n = 484
## [1] "la"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006241 0.026681 0.234 0.815
## BarRestrict -0.091646 0.111911 -0.819 0.413
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.690 7.903 40.91 <2e-16 ***
## s(time_value):BarRestrict 3.199 3.422 4.65 0.04 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.558 Deviance explained = 56.9%
## GCV = 0.000139 Scale est. = 0.00013544 n = 484
## [1] "ma"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.009732 0.036557 -0.266 0.790
## BarRestrict -0.085045 0.493255 -0.172 0.863
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.622 6.932 17.707 < 2e-16 ***
## s(time_value):BarRestrict 4.833 5.098 4.889 0.00018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.597 Deviance explained = 60.7%
## GCV = 0.00020619 Scale est. = 0.00020067 n = 484
## [1] "md"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1444 0.1788 -0.808 0.420
## BarRestrict -0.1723 0.8301 -0.208 0.836
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.00 9.00 26.432 < 2e-16 ***
## s(time_value):BarRestrict 3.35 3.79 6.434 0.000403 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.647 Deviance explained = 65.7%
## GCV = 0.00016691 Scale est. = 0.00016213 n = 484
## [1] "me"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05775 0.01612 3.582 0.000377 ***
## BarRestrict -0.04014 0.03748 -1.071 0.284746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.277 8.614 22.218 <2e-16 ***
## s(time_value):BarRestrict 1.883 1.978 0.523 0.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.479 Deviance explained = 49.1%
## GCV = 0.00015862 Scale est. = 0.0001548 n = 484
## [1] "mi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.059685 0.003739 15.961 <2e-16 ***
## BarRestrict 0.013888 0.441789 0.031 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.245 8.668 39.32 < 2e-16 ***
## s(time_value):BarRestrict 4.897 5.167 13.28 1.87e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.621 Deviance explained = 63.2%
## GCV = 0.00013358 Scale est. = 0.00012954 n = 484
## [1] "mn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06324 0.10554 -0.599 0.549
## BarRestrict -0.11848 0.71213 -0.166 0.868
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.523 7.620 69.512 < 2e-16 ***
## s(time_value):BarRestrict 4.537 4.822 4.903 0.000152 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.695 Deviance explained = 70.3%
## GCV = 0.00022387 Scale est. = 0.0002176 n = 484
## [1] "mo"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01137 0.01932 -0.589 0.556
## BarRestrict -0.16084 0.02748 -5.854 8.99e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.922 8.997 36.81 < 2e-16 ***
## s(time_value):BarRestrict 1.501 1.502 13.58 1.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.603 Deviance explained = 61.2%
## GCV = 0.00022631 Scale est. = 0.00022074 n = 484
## [1] "ms"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04242 0.01768 -2.399 0.0168 *
## BarRestrict -0.16771 0.03962 -4.234 2.77e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.917 8.969 31.88 < 2e-16 ***
## s(time_value):BarRestrict 1.626 1.713 17.94 2.54e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.533 Deviance explained = 54.4%
## GCV = 0.00015517 Scale est. = 0.0001513 n = 484
## [1] "mt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.001885 0.031777 -0.059 0.953
## BarRestrict -0.087036 0.161579 -0.539 0.590
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.733 7.872 32.161 < 2e-16 ***
## s(time_value):BarRestrict 3.552 3.751 7.198 0.000793 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.455 Deviance explained = 46.8%
## GCV = 0.00010101 Scale est. = 9.8344e-05 n = 484
## [1] "nc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03120 0.02206 1.414 0.158
## BarRestrict -0.03424 0.21716 -0.158 0.875
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.501 6.856 19.282 <2e-16 ***
## s(time_value):BarRestrict 4.504 4.710 1.294 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.491 Deviance explained = 50.3%
## GCV = 0.00011851 Scale est. = 0.00011545 n = 484
## [1] "nd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0717860 0.0008127 88.326 < 2e-16 ***
## BarRestrict -0.0504947 0.0096423 -5.237 2.47e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.766 8.983 78.61 < 2e-16 ***
## s(time_value):BarRestrict 1.500 1.500 10.69 0.000139 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.635 Deviance explained = 64.4%
## GCV = 0.00025306 Scale est. = 0.0002469 n = 484
## [1] "ne"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.051559 0.003205 16.088 <2e-16 ***
## BarRestrict -0.122109 0.384819 -0.317 0.751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.00 9.00 56.71 < 2e-16 ***
## s(time_value):BarRestrict 4.35 4.54 16.71 5.81e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.651 Deviance explained = 66.1%
## GCV = 0.00025008 Scale est. = 0.0002424 n = 484
## [1] "nh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.073971 0.002128 34.762 <2e-16 ***
## BarRestrict 0.009128 0.056348 0.162 0.871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.395 8.700 29.00 < 2e-16 ***
## s(time_value):BarRestrict 2.614 3.038 11.62 1.33e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.579 Deviance explained = 58.9%
## GCV = 0.00022965 Scale est. = 0.00022372 n = 484
## [1] "nj"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05593 0.02746 2.037 0.0422 *
## BarRestrict -0.06817 0.11574 -0.589 0.5561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.153 7.474 24.853 <2e-16 ***
## s(time_value):BarRestrict 3.360 3.567 1.423 0.377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.66 Deviance explained = 66.8%
## GCV = 0.00025959 Scale est. = 0.00025314 n = 484
## [1] "nm"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01689 0.02668 0.633 0.527
## BarRestrict -0.05616 0.10405 -0.540 0.590
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 21.584 <2e-16 ***
## s(time_value):BarRestrict 2.304 2.566 0.749 0.446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.423 Deviance explained = 43.7%
## GCV = 5.7254e-05 Scale est. = 5.5739e-05 n = 484
## [1] "nv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003543 0.020360 0.174 0.862
## BarRestrict -0.053842 0.148758 -0.362 0.718
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.048 7.282 25.571 < 2e-16 ***
## s(time_value):BarRestrict 4.219 4.405 6.478 1.49e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.499 Deviance explained = 51.2%
## GCV = 5.5745e-05 Scale est. = 5.4275e-05 n = 484
## [1] "ny"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07818 0.01601 4.884 1.43e-06 ***
## BarRestrict -0.02947 0.04744 -0.621 0.535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.386 7.853 25.279 <2e-16 ***
## s(time_value):BarRestrict 2.524 2.695 0.193 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.587 Deviance explained = 59.6%
## GCV = 0.00018043 Scale est. = 0.00017618 n = 484
## [1] "oh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06398 0.02247 2.847 0.00461 **
## BarRestrict -0.04116 0.12421 -0.331 0.74049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.666 7.015 36.386 <2e-16 ***
## s(time_value):BarRestrict 3.938 4.120 0.363 0.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.628 Deviance explained = 63.7%
## GCV = 0.00016006 Scale est. = 0.00015605 n = 484
## [1] "ok"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0625216 0.0006553 95.41 < 2e-16 ***
## BarRestrict -0.0310145 0.0088615 -3.50 0.00051 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.621 8.959 46.009 <2e-16 ***
## s(time_value):BarRestrict 1.500 1.500 3.867 0.017 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.532 Deviance explained = 54.2%
## GCV = 0.00017035 Scale est. = 0.00016625 n = 484
## [1] "or"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01973 0.06410 -0.308 0.758
## BarRestrict -0.10270 0.27150 -0.378 0.705
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 23.622 <2e-16 ***
## s(time_value):BarRestrict 2.736 3.053 2.066 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.534 Deviance explained = 54.6%
## GCV = 8.4022e-05 Scale est. = 8.1724e-05 n = 484
## [1] "pa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.008371 0.031549 -0.265 0.791
## BarRestrict -0.109970 0.154575 -0.711 0.477
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.464 7.676 49.713 < 2e-16 ***
## s(time_value):BarRestrict 3.621 3.830 7.356 0.000453 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.651 Deviance explained = 66%
## GCV = 0.00014257 Scale est. = 0.00013886 n = 484
## [1] "ri"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02563 0.02644 0.969 0.333
## BarRestrict -0.06414 0.22977 -0.279 0.780
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.584 6.926 27.242 <2e-16 ***
## s(time_value):BarRestrict 4.415 4.611 2.279 0.0271 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.592 Deviance explained = 60.2%
## GCV = 0.00015982 Scale est. = 0.0001557 n = 484
## [1] "sc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02804 0.01764 1.590 0.113
## BarRestrict -0.06233 0.05361 -1.163 0.246
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.473 8.630 20.748 <2e-16 ***
## s(time_value):BarRestrict 2.141 2.339 1.459 0.168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.474 Deviance explained = 48.6%
## GCV = 0.00010309 Scale est. = 0.00010051 n = 484
## [1] "sd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0679938 0.0006775 100.365 <2e-16 ***
## BarRestrict -0.0390968 0.0346925 -1.127 0.26
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.522 8.935 67.753 <2e-16 ***
## s(time_value):BarRestrict 1.500 1.500 2.634 0.051 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.572 Deviance explained = 58.2%
## GCV = 0.00021684 Scale est. = 0.00021168 n = 484
## [1] "tn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.059245 0.002368 25.017 <2e-16 ***
## BarRestrict -0.005724 0.072596 -0.079 0.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.677 8.273 26.293 < 2e-16 ***
## s(time_value):BarRestrict 3.756 4.131 9.498 1.46e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.54 Deviance explained = 55.2%
## GCV = 0.0001462 Scale est. = 0.00014229 n = 484
## [1] "tx"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.09823 0.04560 -2.154 0.0317 *
## BarRestrict -0.18027 0.28886 -0.624 0.5329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.443 7.576 57.03 < 2e-16 ***
## s(time_value):BarRestrict 4.076 4.290 17.27 2.01e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.646 Deviance explained = 65.5%
## GCV = 0.00013562 Scale est. = 0.00013198 n = 484
## [1] "ut"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03692 0.01713 2.155 0.0317 *
## BarRestrict -0.06562 0.04038 -1.625 0.1048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.710 8.858 10.177 1.44e-14 ***
## s(time_value):BarRestrict 1.749 1.856 1.824 0.118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.434 Deviance explained = 44.7%
## GCV = 0.00013842 Scale est. = 0.000135 n = 484
## [1] "va"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10282 0.06271 -1.640 0.102
## BarRestrict -0.12158 0.76866 -0.158 0.874
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.173 7.311 37.17 < 2e-16 ***
## s(time_value):BarRestrict 4.926 5.187 16.61 1.47e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.622 Deviance explained = 63.2%
## GCV = 0.00013513 Scale est. = 0.00013133 n = 484
## [1] "vt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07608 0.01313 5.795 1.25e-08 ***
## BarRestrict -0.01996 0.03049 -0.655 0.513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.922 8.385 41.509 <2e-16 ***
## s(time_value):BarRestrict 1.985 2.084 0.344 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.587 Deviance explained = 59.6%
## GCV = 0.00012899 Scale est. = 0.00012595 n = 484
## [1] "wa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01117 0.02463 0.454 0.650
## BarRestrict -0.04371 0.21917 -0.199 0.842
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.707 7.012 20.569 < 2e-16 ***
## s(time_value):BarRestrict 4.525 4.759 4.473 0.000374 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.603 Deviance explained = 61.3%
## GCV = 9.4203e-05 Scale est. = 9.1725e-05 n = 484
## [1] "wi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0744757 0.0007418 100.397 <2e-16 ***
## BarRestrict -0.0344313 0.4516075 -0.076 0.939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.895 8.996 103.254 < 2e-16 ***
## s(time_value):BarRestrict 2.466 2.653 8.922 0.00107 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.696 Deviance explained = 70.3%
## GCV = 0.00021783 Scale est. = 0.00021204 n = 484
## [1] "wv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03111 0.01674 1.858 0.0638 .
## BarRestrict -0.08081 0.03611 -2.238 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.77 8.902 40.303 <2e-16 ***
## s(time_value):BarRestrict 1.68 1.764 2.663 0.0461 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.582 Deviance explained = 59.1%
## GCV = 0.00013673 Scale est. = 0.00013335 n = 484
## [1] "wy"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ BarRestrict + s(time_value) + s(time_value, by = BarRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01171 0.02110 -0.555 0.579
## BarRestrict -0.10247 0.07419 -1.381 0.168
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.531 8.631 28.153 < 2e-16 ***
## s(time_value):BarRestrict 2.346 2.597 4.105 0.00663 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.505 Deviance explained = 51.7%
## GCV = 9.6592e-05 Scale est. = 9.4122e-05 n = 484Restaurant Restriction
# compute the number of policies and rolling mean of the number
# for each day between start and end dates
for(state in states){
print(state)
print("---------------------")
ftime.state <- ftime %>% filter(geo_value == state)
policy.state <- policy %>% filter(StatePostal == state)
policy_signal.state <- getSumOfPolicy(policy.state, STARTDATE, ENDDATE)
# left join mobility with policy signal by time
ftime.policy.df <- left_join(ftime.state , policy_signal.state, by = "time_value")
# Filter weekend effects
ftime.policy.df<- ftime.policy.df %>%
mutate(weekday= weekdays(as.Date(time_value)))%>%
filter(!weekday %in% c("Saturday", "Sunday"))
ftime.policy.df <- as.data.frame(ftime.policy.df)
ftime.policy.df$time_value <- as.numeric(ftime.policy.df$time_value)
lm.fit <- gam(value~ RestaurantRestrict + s(time_value)+ s(time_value,by = RestaurantRestrict),data=ftime.policy.df)
print(summary(lm.fit))
}## [1] "ak"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0558057 0.0005208 107.162 < 2e-16 ***
## RestaurantRestrict -0.0210209 0.0057426 -3.661 0.00028 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.881 8.996 18.151 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 5.707 0.00444 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.267 Deviance explained = 28.4%
## GCV = 0.00010067 Scale est. = 9.8203e-05 n = 484
## [1] "al"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03498 0.03501 -0.999 0.318
## RestaurantRestrict -0.16035 0.13461 -1.191 0.234
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.319 8.438 28.09 < 2e-16 ***
## s(time_value):RestaurantRestrict 2.658 2.907 3.77 0.00843 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.523 Deviance explained = 53.4%
## GCV = 0.00021732 Scale est. = 0.00021172 n = 484
## [1] "ar"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01223 0.02417 -0.506 0.613
## RestaurantRestrict -0.12493 0.07676 -1.627 0.104
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.667 8.759 31.498 <2e-16 ***
## s(time_value):RestaurantRestrict 2.089 2.315 3.938 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.547 Deviance explained = 55.8%
## GCV = 0.00016191 Scale est. = 0.00015781 n = 484
## [1] "az"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02833 0.04119 -0.688 0.492
## RestaurantRestrict -0.11168 0.24914 -0.448 0.654
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.845 7.941 19.36 < 2e-16 ***
## s(time_value):RestaurantRestrict 3.704 3.885 11.19 3.91e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.464 Deviance explained = 47.7%
## GCV = 9.7476e-05 Scale est. = 9.4848e-05 n = 484
## [1] "ca"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03594 0.02041 1.761 0.0789 .
## RestaurantRestrict -0.02539 0.19189 -0.132 0.8948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.640 6.959 20.94 <2e-16 ***
## s(time_value):RestaurantRestrict 4.555 4.789 2.33 0.0231 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.486 Deviance explained = 49.9%
## GCV = 7.1385e-05 Scale est. = 6.9513e-05 n = 484
## [1] "co"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006166 0.031562 0.195 0.845
## RestaurantRestrict -0.066743 0.257869 -0.259 0.796
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.866 7.134 19.278 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.442 4.670 3.567 0.00257 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.531 Deviance explained = 54.3%
## GCV = 0.00013074 Scale est. = 0.00012728 n = 484
## [1] "ct"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06736 0.02025 3.327 0.000947 ***
## RestaurantRestrict -0.05636 0.04610 -1.222 0.222156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.234 8.581 20.700 <2e-16 ***
## s(time_value):RestaurantRestrict 1.905 1.999 0.756 0.464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.587 Deviance explained = 59.6%
## GCV = 0.00024418 Scale est. = 0.00023831 n = 484
## [1] "dc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.073425 0.002304 31.866 < 2e-16 ***
## RestaurantRestrict -0.016741 0.002877 -5.819 1.08e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 2.991 3.887 36.82 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 95.07 4.69e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.482 Deviance explained = 48.7%
## GCV = 0.00012529 Scale est. = 0.00012374 n = 484
## [1] "de"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07416 0.01126 6.583 1.23e-10 ***
## RestaurantRestrict -0.01967 0.04507 -0.436 0.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.234 6.837 32.599 <2e-16 ***
## s(time_value):RestaurantRestrict 3.243 3.494 0.187 0.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.585 Deviance explained = 59.4%
## GCV = 0.00014782 Scale est. = 0.00014447 n = 484
## [1] "fl"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.060754 0.001507 40.314 <2e-16 ***
## RestaurantRestrict -0.005974 0.168376 -0.035 0.972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.804 7.637 21.72 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.505 4.683 7.18 2.1e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.504 Deviance explained = 51.6%
## GCV = 9.519e-05 Scale est. = 9.2671e-05 n = 484
## [1] "ga"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05909 0.05770 -1.024 0.306
## RestaurantRestrict -0.17784 0.36273 -0.490 0.624
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.993 8.097 28.91 < 2e-16 ***
## s(time_value):RestaurantRestrict 3.677 3.873 23.46 4.95e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.533 Deviance explained = 54.4%
## GCV = 0.00013826 Scale est. = 0.0001345 n = 484
## [1] "hi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.033960 0.002507 13.544 <2e-16 ***
## RestaurantRestrict -0.003922 0.003276 -1.197 0.232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.873 7.894 20.06 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.512 1.515 10.64 0.00127 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.298 Deviance explained = 31.1%
## GCV = 1.482e-05 Scale est. = 1.4518e-05 n = 484
## [1] "ia"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01479 0.02465 0.600 0.5488
## RestaurantRestrict -0.13579 0.05192 -2.615 0.0092 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.852 8.939 50.308 <2e-16 ***
## s(time_value):RestaurantRestrict 1.647 1.730 4.105 0.0128 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.666 Deviance explained = 67.4%
## GCV = 0.00028065 Scale est. = 0.00027369 n = 484
## [1] "id"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04658 0.05859 -0.795 0.427
## RestaurantRestrict 0.05546 1.25528 0.044 0.965
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.101 7.25 28.89 < 2e-16 ***
## s(time_value):RestaurantRestrict 5.300 5.45 12.07 1.04e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.477 Deviance explained = 49.1%
## GCV = 9.9037e-05 Scale est. = 9.6193e-05 n = 484
## [1] "il"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03831 0.02857 1.341 0.181
## RestaurantRestrict -0.04652 0.60799 -0.077 0.939
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.29 6.670 51.889 <2e-16 ***
## s(time_value):RestaurantRestrict 5.02 5.279 1.607 0.126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.693 Deviance explained = 70%
## GCV = 0.00021463 Scale est. = 0.00020895 n = 484
## [1] "in"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02705 0.02667 1.014 0.311
## RestaurantRestrict -0.04771 0.18315 -0.261 0.795
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.798 7.104 33.637 <2e-16 ***
## s(time_value):RestaurantRestrict 4.236 4.434 1.524 0.152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.573 Deviance explained = 58.3%
## GCV = 0.00012944 Scale est. = 0.00012609 n = 484
## [1] "ks"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.068501 0.000742 92.318 < 2e-16 ***
## RestaurantRestrict -0.044873 0.010462 -4.289 2.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.483 8.925 60.188 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 6.771 0.00208 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.593 Deviance explained = 60.2%
## GCV = 0.00022294 Scale est. = 0.00021765 n = 484
## [1] "ky"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06291 0.01789 3.517 0.000478 ***
## RestaurantRestrict -0.04747 0.03739 -1.270 0.204787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.468 8.749 32.89 <2e-16 ***
## s(time_value):RestaurantRestrict 1.764 1.838 0.57 0.454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.585 Deviance explained = 59.4%
## GCV = 0.0001815 Scale est. = 0.0001771 n = 484
## [1] "la"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006241 0.026681 0.234 0.815
## RestaurantRestrict -0.091646 0.111911 -0.819 0.413
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.690 7.903 40.91 <2e-16 ***
## s(time_value):RestaurantRestrict 3.199 3.422 4.65 0.04 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.558 Deviance explained = 56.9%
## GCV = 0.000139 Scale est. = 0.00013544 n = 484
## [1] "ma"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.009732 0.036557 -0.266 0.790
## RestaurantRestrict -0.085045 0.493255 -0.172 0.863
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.622 6.932 17.707 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.833 5.098 4.889 0.00018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.597 Deviance explained = 60.7%
## GCV = 0.00020619 Scale est. = 0.00020067 n = 484
## [1] "md"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1444 0.1788 -0.808 0.420
## RestaurantRestrict -0.1723 0.8301 -0.208 0.836
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.00 9.00 26.432 < 2e-16 ***
## s(time_value):RestaurantRestrict 3.35 3.79 6.434 0.000403 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.647 Deviance explained = 65.7%
## GCV = 0.00016691 Scale est. = 0.00016213 n = 484
## [1] "me"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05775 0.01612 3.582 0.000377 ***
## RestaurantRestrict -0.04014 0.03748 -1.071 0.284746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.277 8.614 22.218 <2e-16 ***
## s(time_value):RestaurantRestrict 1.883 1.978 0.523 0.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.479 Deviance explained = 49.1%
## GCV = 0.00015862 Scale est. = 0.0001548 n = 484
## [1] "mi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.059685 0.003739 15.961 <2e-16 ***
## RestaurantRestrict 0.013888 0.441789 0.031 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.245 8.668 39.32 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.897 5.167 13.28 1.87e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.621 Deviance explained = 63.2%
## GCV = 0.00013358 Scale est. = 0.00012954 n = 484
## [1] "mn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06324 0.10554 -0.599 0.549
## RestaurantRestrict -0.11848 0.71213 -0.166 0.868
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.523 7.620 69.512 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.537 4.822 4.903 0.000152 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.695 Deviance explained = 70.3%
## GCV = 0.00022387 Scale est. = 0.0002176 n = 484
## [1] "mo"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01137 0.01932 -0.589 0.556
## RestaurantRestrict -0.16084 0.02748 -5.854 8.99e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.922 8.997 36.81 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.501 1.502 13.58 1.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.603 Deviance explained = 61.2%
## GCV = 0.00022631 Scale est. = 0.00022074 n = 484
## [1] "ms"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04242 0.01768 -2.399 0.0168 *
## RestaurantRestrict -0.16771 0.03962 -4.234 2.77e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.917 8.969 31.88 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.626 1.713 17.94 2.54e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.533 Deviance explained = 54.4%
## GCV = 0.00015517 Scale est. = 0.0001513 n = 484
## [1] "mt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.001885 0.031777 -0.059 0.953
## RestaurantRestrict -0.087036 0.161579 -0.539 0.590
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.733 7.872 32.161 < 2e-16 ***
## s(time_value):RestaurantRestrict 3.552 3.751 7.198 0.000793 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.455 Deviance explained = 46.8%
## GCV = 0.00010101 Scale est. = 9.8344e-05 n = 484
## [1] "nc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03120 0.02206 1.414 0.158
## RestaurantRestrict -0.03424 0.21716 -0.158 0.875
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.501 6.856 19.282 <2e-16 ***
## s(time_value):RestaurantRestrict 4.504 4.710 1.294 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.491 Deviance explained = 50.3%
## GCV = 0.00011851 Scale est. = 0.00011545 n = 484
## [1] "nd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0717860 0.0008127 88.326 < 2e-16 ***
## RestaurantRestrict -0.0504947 0.0096423 -5.237 2.47e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.766 8.983 78.61 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 10.69 0.000139 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.635 Deviance explained = 64.4%
## GCV = 0.00025306 Scale est. = 0.0002469 n = 484
## [1] "ne"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.051559 0.003205 16.088 <2e-16 ***
## RestaurantRestrict -0.122109 0.384819 -0.317 0.751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.00 9.00 56.71 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.35 4.54 16.71 5.81e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.651 Deviance explained = 66.1%
## GCV = 0.00025008 Scale est. = 0.0002424 n = 484
## [1] "nh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.073971 0.002128 34.762 <2e-16 ***
## RestaurantRestrict 0.009128 0.056348 0.162 0.871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.395 8.700 29.00 < 2e-16 ***
## s(time_value):RestaurantRestrict 2.614 3.038 11.62 1.33e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.579 Deviance explained = 58.9%
## GCV = 0.00022965 Scale est. = 0.00022372 n = 484
## [1] "nj"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05593 0.02746 2.037 0.0422 *
## RestaurantRestrict -0.06817 0.11574 -0.589 0.5561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.153 7.474 24.853 <2e-16 ***
## s(time_value):RestaurantRestrict 3.360 3.567 1.423 0.377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.66 Deviance explained = 66.8%
## GCV = 0.00025959 Scale est. = 0.00025314 n = 484
## [1] "nm"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01689 0.02668 0.633 0.527
## RestaurantRestrict -0.05616 0.10405 -0.540 0.590
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 21.584 <2e-16 ***
## s(time_value):RestaurantRestrict 2.304 2.566 0.749 0.446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.423 Deviance explained = 43.7%
## GCV = 5.7254e-05 Scale est. = 5.5739e-05 n = 484
## [1] "nv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003543 0.020360 0.174 0.862
## RestaurantRestrict -0.053842 0.148758 -0.362 0.718
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.048 7.282 25.571 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.219 4.405 6.478 1.49e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.499 Deviance explained = 51.2%
## GCV = 5.5745e-05 Scale est. = 5.4275e-05 n = 484
## [1] "ny"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07818 0.01601 4.884 1.43e-06 ***
## RestaurantRestrict -0.02947 0.04744 -0.621 0.535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.386 7.853 25.279 <2e-16 ***
## s(time_value):RestaurantRestrict 2.524 2.695 0.193 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.587 Deviance explained = 59.6%
## GCV = 0.00018043 Scale est. = 0.00017618 n = 484
## [1] "oh"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06398 0.02247 2.847 0.00461 **
## RestaurantRestrict -0.04116 0.12421 -0.331 0.74049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.666 7.015 36.386 <2e-16 ***
## s(time_value):RestaurantRestrict 3.938 4.120 0.363 0.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.628 Deviance explained = 63.7%
## GCV = 0.00016006 Scale est. = 0.00015605 n = 484
## [1] "ok"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0625216 0.0006553 95.41 < 2e-16 ***
## RestaurantRestrict -0.0310145 0.0088615 -3.50 0.00051 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.621 8.959 46.009 <2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 3.867 0.017 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.532 Deviance explained = 54.2%
## GCV = 0.00017035 Scale est. = 0.00016625 n = 484
## [1] "or"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01973 0.06410 -0.308 0.758
## RestaurantRestrict -0.10270 0.27150 -0.378 0.705
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 9.000 9.000 23.622 <2e-16 ***
## s(time_value):RestaurantRestrict 2.736 3.053 2.066 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.534 Deviance explained = 54.6%
## GCV = 8.4022e-05 Scale est. = 8.1724e-05 n = 484
## [1] "pa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.008371 0.031549 -0.265 0.791
## RestaurantRestrict -0.109970 0.154575 -0.711 0.477
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.464 7.676 49.713 < 2e-16 ***
## s(time_value):RestaurantRestrict 3.621 3.830 7.356 0.000453 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.651 Deviance explained = 66%
## GCV = 0.00014257 Scale est. = 0.00013886 n = 484
## [1] "ri"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02563 0.02644 0.969 0.333
## RestaurantRestrict -0.06414 0.22977 -0.279 0.780
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.584 6.926 27.242 <2e-16 ***
## s(time_value):RestaurantRestrict 4.415 4.611 2.279 0.0271 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.592 Deviance explained = 60.2%
## GCV = 0.00015982 Scale est. = 0.0001557 n = 484
## [1] "sc"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.061902 0.001013 61.078 < 2e-16 ***
## RestaurantRestrict -0.026051 0.003367 -7.738 6.17e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.429 8.913 21.48 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 15.02 7.52e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.465 Deviance explained = 47.6%
## GCV = 0.00010472 Scale est. = 0.00010225 n = 484
## [1] "sd"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0679938 0.0006775 100.365 <2e-16 ***
## RestaurantRestrict -0.0390968 0.0346925 -1.127 0.26
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.522 8.935 67.753 <2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 2.634 0.051 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.572 Deviance explained = 58.2%
## GCV = 0.00021684 Scale est. = 0.00021168 n = 484
## [1] "tn"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0628888 0.0006158 102.126 < 2e-16 ***
## RestaurantRestrict -0.0406676 0.0075931 -5.356 1.33e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.662 8.966 43.452 < 2e-16 ***
## s(time_value):RestaurantRestrict 1.500 1.500 9.813 0.000252 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.535 Deviance explained = 54.5%
## GCV = 0.00014753 Scale est. = 0.00014397 n = 484
## [1] "tx"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.09823 0.04560 -2.154 0.0317 *
## RestaurantRestrict -0.18027 0.28886 -0.624 0.5329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.443 7.576 57.03 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.076 4.290 17.27 2.01e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.646 Deviance explained = 65.5%
## GCV = 0.00013562 Scale est. = 0.00013198 n = 484
## [1] "ut"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03692 0.01713 2.155 0.0317 *
## RestaurantRestrict -0.06562 0.04038 -1.625 0.1048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.710 8.858 10.177 1.44e-14 ***
## s(time_value):RestaurantRestrict 1.749 1.856 1.824 0.118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.434 Deviance explained = 44.7%
## GCV = 0.00013842 Scale est. = 0.000135 n = 484
## [1] "va"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03702 0.02007 1.845 0.0657 .
## RestaurantRestrict -0.06380 0.06379 -1.000 0.3178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.134 8.359 26.109 <2e-16 ***
## s(time_value):RestaurantRestrict 2.397 2.596 1.095 0.254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.607 Deviance explained = 61.6%
## GCV = 0.00014014 Scale est. = 0.00013666 n = 484
## [1] "vt"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07608 0.01313 5.795 1.25e-08 ***
## RestaurantRestrict -0.01996 0.03049 -0.655 0.513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 7.922 8.385 41.509 <2e-16 ***
## s(time_value):RestaurantRestrict 1.985 2.084 0.344 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.587 Deviance explained = 59.6%
## GCV = 0.00012899 Scale est. = 0.00012595 n = 484
## [1] "wa"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01117 0.02463 0.454 0.650
## RestaurantRestrict -0.04371 0.21917 -0.199 0.842
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 6.707 7.012 20.569 < 2e-16 ***
## s(time_value):RestaurantRestrict 4.525 4.759 4.473 0.000374 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.603 Deviance explained = 61.3%
## GCV = 9.4203e-05 Scale est. = 9.1725e-05 n = 484
## [1] "wi"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0744757 0.0007418 100.397 <2e-16 ***
## RestaurantRestrict -0.0344313 0.4516075 -0.076 0.939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.895 8.996 103.254 < 2e-16 ***
## s(time_value):RestaurantRestrict 2.466 2.653 8.922 0.00107 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.696 Deviance explained = 70.3%
## GCV = 0.00021783 Scale est. = 0.00021204 n = 484
## [1] "wv"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03111 0.01674 1.858 0.0638 .
## RestaurantRestrict -0.08081 0.03611 -2.238 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.77 8.902 40.303 <2e-16 ***
## s(time_value):RestaurantRestrict 1.68 1.764 2.663 0.0461 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.582 Deviance explained = 59.1%
## GCV = 0.00013673 Scale est. = 0.00013335 n = 484
## [1] "wy"
## [1] "---------------------"
##
## Family: gaussian
## Link function: identity
##
## Formula:
## value ~ RestaurantRestrict + s(time_value) + s(time_value, by = RestaurantRestrict)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01171 0.02110 -0.555 0.579
## RestaurantRestrict -0.10247 0.07419 -1.381 0.168
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(time_value) 8.531 8.631 28.153 < 2e-16 ***
## s(time_value):RestaurantRestrict 2.346 2.597 4.105 0.00663 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Rank: 20/21
## R-sq.(adj) = 0.505 Deviance explained = 51.7%
## GCV = 9.6592e-05 Scale est. = 9.4122e-05 n = 484